interview – Best DevOps

7 DevOps Engineer Interview Questions That Reveal True Expertise

anil — Tue, 21 Jan 2020 06:07:54 +0000

Original source: enterprisersproject.com. This article expands on and updates the core questions for a DevOps engineering audience.

Why Hiring a Great DevOps Engineer Is So Difficult

DevOps engineers consistently rank among the most sought-after roles in IT. They occupy a unique intersection of software engineering, systems administration, security awareness, and interpersonal communication. Because they bridge so many disciplines, a resume alone rarely tells you whether a candidate can actually do the job under pressure.

The questions below are designed to surface the behaviours, attitudes, and real-world experience that separate genuinely capable candidates from those who have simply memorized the right buzzwords.

What Makes a Great DevOps Engineer?

DevOps engineers typically come from a background in software or systems engineering. What elevates them is the ability to hold technical and human concerns in the same hand: they can write a Terraform module in the morning and facilitate a cross-team retrospective in the afternoon. They are the connective tissue between development, operations, security, and the business.

With that profile in mind, here are seven interview questions — and the reasoning behind each — that will help you find your next hire.

The 7 Interview Questions

1. How do you factor security into your DevOps process? How have you helped past teams develop greater security awareness?

Why ask this: DevOps automation accelerates delivery — and it can propagate vulnerabilities just as fast. Security must be built into the pipeline from the start (often called a “shift-left” approach), not bolted on after a release.

What a strong answer looks like: The candidate should describe concrete practices rather than abstract principles. Look for references to integrating static analysis (SAST) or dependency scanning tools into CI/CD pipelines, using secrets management solutions (HashiCorp Vault, AWS Secrets Manager), enforcing least-privilege IAM policies, or running automated compliance checks as part of the build. Bonus points if they describe how they brought the wider team along — lunch-and-learns, blameless post-mortems, or pairing sessions — because security awareness is a cultural problem as much as a technical one.

Red flag: A candidate who frames security as someone else’s problem (“that’s what the security team is for”) is likely to create gaps in the pipeline.

2. What do you always automate? What do you consciously choose not to automate? Describe a process that was particularly hard to automate.

Why ask this: Automation is the cornerstone of DevOps, but indiscriminate automation creates brittle, hard-to-debug systems. A mature engineer knows where to draw the line.

What a strong answer looks like: Expect fluency with CI/CD platforms (Jenkins, GitHub Actions, GitLab CI, Azure DevOps) and Infrastructure as Code tools (Terraform, Pulumi, Ansible, CloudFormation). More revealing is their answer on what not to automate: one-off migrations, processes with high human-judgment requirements, or situations where the cost of maintaining automation exceeds its benefit. The “hard to automate” question is a storytelling prompt — look for a real anecdote with a concrete problem, the approach taken, and lessons learned.

3. Describe your ideal working environment for a DevOps team. What have you learned from working closely with operations?

Why ask this: DevOps is fundamentally collaborative. A candidate who thrives in isolation, or who dismisses operations concerns as blockers rather than inputs, will struggle.

What a strong answer looks like: Listen for references to psychological safety, shared on-call responsibilities, documented runbooks, and regular cross-functional communication (daily standups, shared dashboards, joint retrospectives). A candidate who has genuinely worked closely with operations will speak to specific tensions they navigated — for example, balancing deployment velocity against change-management processes — and explain how they resolved them collaboratively rather than unilaterally.

4. Tell me about scripts you’ve developed that were critical to your team’s success.

Why ask this: Scripting is the practical multiplier for a DevOps engineer. Even those who are not full software developers need to write reliable, maintainable automation code.

What a strong answer looks like: Strong candidates will have specific examples — not vague statements about “writing a lot of Python.” They should be able to explain the business problem the script solved, why they chose that language (Python, Bash, PowerShell, Go), how they tested and documented it, and how it was handed off so the team could maintain it without them. Maintainability is as important as functionality; ask a follow-up question about how the script was documented or version-controlled.

5. Tell me about a release that went badly wrong. How did you stabilize the environment, and what did you learn?

Why ask this: Incidents are inevitable. How an engineer behaves under pressure — and what they take away from the experience — reveals their professional maturity more than any success story.

What a strong answer looks like: Look for a structured, honest account: what happened, how they diagnosed it, what decision they made under pressure, and what the outcome was. Crucially, listen for a clear “what I learned” section. Did they implement a post-mortem process? Add a missing test? Improve rollback automation? A candidate who blames others or becomes defensive is a warning sign. One who treats the incident as a learning opportunity — and can point to a concrete improvement that resulted — is exactly who you want.

6. How do you measure success — both for yourself and for the DevOps team as a whole?

Why ask this: Without measurable goals, improvement is just a feeling. A DevOps engineer who can define, track, and act on metrics is far more valuable than one who operates on intuition alone.

What a strong answer looks like: Candidates familiar with DevOps measurement will reference metrics such as deployment frequency, lead time for changes, mean time to recovery (MTTR), and change failure rate — the four key metrics popularised by the DORA research programme. They should also be able to discuss what baseline they started from, what targets the team set, and how they tracked progress over time. Personal success metrics are equally telling: look for a growth mindset rather than a focus on individual heroics.

7. What are your favourite DevOps tools right now? What new tools or features have you explored recently?

Why ask this: The DevOps toolchain evolves rapidly. An engineer who stopped learning two years ago is already behind. Genuine curiosity is one of the best leading indicators of long-term performance.

What a strong answer looks like: You want specific enthusiasm, not a rehearsed list. A great candidate will explain why a tool works well for them — the problem it solves, the trade-offs compared to alternatives, what they find genuinely elegant about it. The “recently explored” portion is the most revealing: it shows whether they self-direct their learning or wait to be trained. Topics worth listening for include platform engineering, GitOps workflows, eBPF-based observability, AI-assisted coding in the development workflow, or advances in container orchestration and service mesh technology.

Putting It All Together

No single question will surface everything you need to know, but together these seven prompts test the full profile of a high-performing DevOps engineer: security mindset, automation judgement, collaborative instinct, scripting craft, incident resilience, data-driven improvement, and an appetite for continuous learning.

Supplement these behavioural questions with a practical exercise — reviewing a sample CI/CD pipeline, writing a short script, or walking through an architecture diagram — to validate what you hear in conversation. The strongest candidates will welcome the opportunity to show their work.

The DevOps talent market remains competitive. Asking sharper questions is the most reliable way to close the gap between a good-sounding candidate and a great hire.

Is DevOps in an Identity Crisis? Viktor Farcic Weighs In

anil — Mon, 20 Jan 2020 06:20:51 +0000

This article draws on perspectives shared by Viktor Farcic in DevOps Paradox, a book in which Farcic interviews industry colleagues about what DevOps means to them in practice. The original source was attributed to gotechdaily.com.

The Definitional Problem at the Heart of DevOps

Ask ten engineers what DevOps is and you will likely get ten different answers. That ambiguity is not a sign of immaturity in the field — according to Viktor Farcic, it is baked into the nature of DevOps itself. Farcic, a long-time DevOps practitioner and author, explores this tension at length in DevOps Paradox, a book structured as a series of candid conversations with practitioners across the industry.

The core irony is pointed: DevOps exists to bring order and collaboration to the chaotic, siloed world of software delivery — yet it cannot agree on what it actually is.

What DevOps Actually Is, According to Farcic

Farcic’s answer to “What is DevOps?” is deliberately minimal:

“Almost everyone gives a different answer to the question ‘What is DevOps?’ — there is a big discrepancy between the idea and the implementation. In my opinion, the main goal of DevOps is to enable self-sufficient, product-oriented teams that are able to take complete control of their products.”
Viktor Farcic, DevOps Paradox

The traditional software delivery model fragments an application’s lifecycle across isolated groups: business analysts define requirements, architects set guidelines, developers write code, testers validate it, and operations teams release it. Each group belongs to a different department with different — and often competing — incentives. Collaboration suffers because teams optimise for their own departmental goals rather than a shared product outcome.

DevOps, in Farcic’s framing, attempts to replace that structure with cross-functional, product-oriented teams that own the entire application lifecycle and report through a single line of accountability. The goal is collective ownership and empathy — not a prescribed workflow.

DevOps Is Neither a Process Nor a Technology Stack

A common misconception is that adopting certain tools — CI/CD pipelines, container orchestration, infrastructure-as-code — makes an organisation “DevOps.” Farcic pushes back on this directly:

“DevOps is neither [a process nor a set of technologies]. There is no prescribed process that has to be followed. Likewise, there is no technology that we can adopt to transform ourselves into ‘DevOps’ teams. It’s just an idea that developers, operators, and everyone else have to work together instead of being isolated in different silos.”
Viktor Farcic, DevOps Paradox

That does not mean tooling is irrelevant. Farcic makes a subtler point: every technology reflects the culture and processes of the team that built it. If an organisation adopts a tool that does not align with its own culture and processes, results will be sub-optimal at best. Teams must either choose technologies that fit their existing culture or use technology deliberately as a lever to shift that culture. Neither element can be ignored in isolation.

Does the “DevOps Engineer” Role Actually Exist?

The job title “DevOps Engineer” has become ubiquitous on job boards and org charts. Farcic is sceptical of what it usually represents:

“I don’t think there is such a thing as a ‘DevOps Engineer.’ The term was invented by people who were unwilling to apply the changes that DevOps leads to. Most of the time, a ‘DevOps Engineer’ is just another name for someone who works in shared services, operations, or infrastructure — in the department that was simply the first to be renamed DevOps.”
Viktor Farcic, DevOps Paradox

This is a provocative but coherent observation. If DevOps is about dissolving silos between development and operations, creating a dedicated “DevOps team” simply adds a new silo — one responsible for automation and pipelines — without changing the underlying organisational dynamics.

Is DevOps Experiencing an Identity Crisis?

Given how widely DevOps is misunderstood and misapplied, it is reasonable to ask whether it is in an identity crisis. Farcic’s answer is that it cannot be, because it never had a fixed identity to begin with:

“No one has defined the process behind DevOps. There are no steps that need to be followed daily or weekly. It’s just an idea that we should work together and not throw things over the walls of the department. The way there is therefore open to interpretation. DevOps never had a clear identity, so there can be no identity crisis.”
Viktor Farcic, DevOps Paradox

Compare this to Agile, which — despite being a set of values and principles — spawned concrete frameworks like Scrum with clearly defined ceremonies, roles, and artefacts. DevOps produced no equivalent. That openness is both its strength (adaptable to many contexts) and its weakness (easy to co-opt or dilute).

The Biggest Challenges Facing DevOps Today

Farcic identifies the core challenges as cultural and organisational rather than technical:

Cosmetic rebranding: Companies rename existing departments — shared services, infrastructure, operations — as “DevOps teams” without changing how they work. The label changes; the silos do not.
Middle management resistance: True DevOps reorganisation flattens hierarchies and eliminates some managerial layers. Those whose authority depends on the current structure have strong incentives to resist.
Expertise and identity threat: Engineers who have spent years specialising in one domain — operations, QA, networking — can feel that a cross-functional model devalues their career path and credentials.

These are not technical problems that can be solved by deploying a new tool. They require deliberate organisational design, executive sponsorship, and a willingness to disrupt existing power structures.

About DevOps Paradox

Farcic conceived DevOps Paradox after noticing that the most valuable conversations at conferences happen informally — in hallways, at dinners, between sessions — rather than in planned talks. He wanted to capture that unstructured, exploratory dialogue and make it accessible to practitioners who cannot attend conferences in person.

“I had no real goals for this book other than talking to people about any subject as long as it was DevOps related. I have not prepared any questions in advance. Instead, I only brought together people I would want to speak to if I met them at a conference.”
Viktor Farcic, DevOps Paradox

The result is a deliberately diverse collection of viewpoints: interviewees from large enterprises and startups, veterans and emerging practitioners, friends and first-time acquaintances. The breadth is intentional — Farcic wanted conflicting opinions, not a unified manifesto.

Key Takeaways for DevOps Practitioners

DevOps is an idea, not a framework. There is no canonical process to implement. Each organisation must define what collaboration across the software lifecycle looks like for its specific context.
Tooling follows culture. Adopting DevOps tools without addressing organisational culture produces marginal improvements at best. Culture and process must evolve together with the technology.
“DevOps team” is often a misnomer. A team called DevOps that sits between development and operations may simply be a rebranded operations team. True DevOps thinking embeds operational responsibility into product teams.
The hard problems are human. Resistance from middle management and specialists whose identity is tied to the old structure are the primary barriers to DevOps adoption — not tool selection or pipeline design.

How Google Cloud Plans to Win the Enterprise: Rob Enslin Explains

anil — Tue, 20 Aug 2019 06:14:09 +0000

This article is based on an interview originally published on diginomica.com in August 2019. Quotes are drawn from that source.

Background: Two Enterprise Veterans Take the Helm

When Google Cloud appointed Rob Enslin as President of Global Customer Operations and Thomas Kurian as CEO, both within a short window, the moves signalled something deliberate. Enslin had spent 27 years at SAP; Kurian had spent 22 years at Oracle. These are not cloud-native executives — they are enterprise software veterans who understand procurement cycles, C-suite priorities, long-term contracts, and the difference between a technology pitch and a business case.

The appointments prompted scepticism in some quarters. AWS and Azure had established dominant market positions, and observers questioned whether Google Cloud Platform (GCP) could realistically close that gap. Enslin and Kurian’s answer, as articulated in this interview, is that GCP does not intend to win purely on infrastructure parity. It intends to win on differentiated, data-driven business outcomes.

The Core Message: Bring Google Your Hardest Problems

Asked what single message he would deliver to enterprise buyers, Enslin was direct:

We are very good at solving the world’s biggest, complex problems. Bring your complex problems to Google Cloud, and we’ll solve them with you.

That framing is deliberate. Rather than competing feature-by-feature on compute pricing or managed Kubernetes offerings, GCP is positioning itself as the vendor that applies Google-scale data intelligence to genuinely hard enterprise problems: risk modelling, real-time analytics across petabyte-scale datasets, AI-driven operational decisions.

On why he left SAP after nearly three decades, Enslin explained:

I felt that there was an opportunity for me to do something really different in the industry. What drove me was the opportunity to build a significant presence in the cloud, with Google’s backing, and with the fabulous engineering talent that we have here. And to build applications that went past just transaction-based applications — but truly have the ability to apply intelligence to massive amounts of data. Google has knowledge of vast tracts of the Internet, and how to operate in that space. Bringing many of the AI and ML technologies, for which Google is well known, to the market. I think we will be able to provide highly differentiated applications.

Moving Away from Technical Solutions Toward Business Solutions

A recurring theme from Google Cloud Next 2019 — GCP’s annual customer conference held in San Francisco — was the deliberate shift in audience. The event was business-led and C-suite focused rather than developer-centric. Two announcements in particular illustrated the strategic direction:

Anthos: A software layer enabling enterprises to deploy and manage applications consistently across any cloud provider or on-premises environment. The product addressed the enterprise demand for multi-cloud flexibility rather than locking buyers into a single infrastructure provider.
Open-source data ecosystem integrations: Google Cloud brought partners including MongoDB and DataStax into the GCP environment with tighter technical integrations, unified billing, and shared support — reducing the operational friction of running those workloads on GCP.

GCP also reorganised its go-to-market structure around industry verticals — including retail, financial services, media, manufacturing, and public sector — reflecting the recognition that enterprise buyers respond better to industry-specific value propositions than to horizontal platform pitches.

Enslin pointed to real-world deployments as evidence that the data-at-scale proposition is already delivering:

I really, truly believe that because of Google’s knowledge of how to deal with data at scale, we can offer something different. Some of the things we do with HSBC, for instance, are just phenomenal — in terms of getting hundreds of petabytes of information to do risk assessments in split seconds, or nanoseconds, versus hours and days. Those are the kinds of things that are extremely disruptive and give companies and industries an opportunity to actually change the game.
It’s really hard to do that with transaction-based systems of record. Yet we can be disruptive with systems of record and with data, like we are doing in companies such as HSBC, Target, Macy’s, and so on.
But you are 100% right. To differentiate ourselves in the market we need to take the technology toolsets we have around big data, smarter analytics, AI and ML, and build solutions to truly differentiate ourselves.

What Keeps Enslin Up at Night: Execution at Speed

Enslin was candid about the internal challenges facing GCP’s enterprise ambitions. The strategic intent is clear; the execution difficulty lies in translating Google’s technical capabilities into packaged solutions that business buyers — not just engineers — can evaluate, procure, and justify to a board.

We are focused on simplifying how we engage with customers, so it’s much easier to understand. We are simplifying our contracts, our pricing. For me, what keeps me up at night is doing many things at speed and being able to execute at speed. We have tremendous support from Alphabet to support Google Cloud in what we are doing.
It’s just a matter of making sure we can hire in the right markets and bring solution sets to the market as fast as possible, and in a way that customers can understand them — in a way that adds tremendous value to them.
I’m always thinking about how do we package the solution sets so that a business leader understands it. So that it’s not a technical solution, it’s actually a business solution. And the value that they will receive is clear in understanding. We want to bring value to the company. Because if someone makes a significant investment in Google Cloud, we want to make sure that they get a significant return for that.

The reference to simplifying contracts and pricing is notable. A persistent criticism of GCP in 2019 was that its commercial terms were less mature than those of AWS or Azure, which had spent years refining enterprise agreement structures, discount tiers, and support frameworks. Enslin’s background in enterprise sales at SAP directly informs this priority.

Building Out Google’s Enterprise Sales Capability

Beyond strategy, Enslin was making structural changes to GCP’s commercial organisation. He stated his intention to triple the company’s sales headcount. It was also reported — though not confirmed in this interview — that sales compensation structures were shifting toward higher variable pay tied to target attainment, consistent with the models used at SAP and Oracle.

Enslin described the scope of that buildout:

We have really focused on, and what I’m trying to bring, is the enterprise knowledge of how customers want to transact with Google, how they want to be supported by Google. I’m building out Google’s presence on a global basis — we have a huge opportunity in Asia, in markets around the world, where we are, I would say, in our infancy.
In a competitive landscape we will continue to compete, in certain cases, with AWS on infrastructure. But it’s more important that we produce solution sets and go to market in a way that touches the customer base.

The acknowledgement that GCP remains in its infancy in several major geographic markets is a useful reality check. AWS and Azure had years of head start in enterprise account coverage across APAC, EMEA, and Latin America. Closing that gap is a years-long investment in people, partner ecosystems, and local compliance infrastructure.

Assessment: Genuine Opportunity, Significant Execution Risk

GCP’s enterprise strategy in 2019 rested on a coherent hypothesis: that data intelligence, applied to industry-specific problems, is a more defensible and more valuable position than competing on infrastructure price and breadth of services alone. The assets supporting that hypothesis are real — Google’s machine learning research, its global network infrastructure, its experience processing data at a scale few organisations approach.

The execution risks, however, are equally real:

Enterprise sales culture: Google’s institutional instincts are product- and engineering-led. Building and sustaining a quota-carrying, relationship-driven enterprise sales organisation requires a different kind of management discipline.
Trust and track record: Enterprise buyers make multi-year infrastructure commitments. GCP’s enterprise track record in 2019 was shorter than AWS’s or Azure’s, making it harder to point to long-standing, mission-critical deployments at scale.
Internal alignment: Shifting a company whose primary revenue model is advertising toward a services and solutions model for the enterprise requires sustained internal commitment — not just from Alphabet leadership, but from the engineering and product organisations that ultimately build what gets sold.
Packaging complexity: Converting sophisticated AI and data capabilities into solution packages that a CFO or COO can evaluate is genuinely difficult, and is an area where enterprise software vendors have historically struggled.

Enslin and Kurian represent a serious attempt to address those gaps. Whether Google Cloud can sustain the organisational commitment required — and persuade enterprise buyers that it is a reliable long-term partner — is the central question that the strategy, however well articulated, cannot answer on its own.

Original interview source: diginomica.com. Interview conducted and published August 2019.

How to Hire and Interview for DevOps Roles: Questions, Tips, and Best Practices

anil — Sat, 05 Aug 2017 07:11:27 +0000

The DevOps job market has become increasingly competitive as engineering leaders look for professionals who can bridge the gap between software development and IT operations. Whether you are building out an interview process or preparing as a candidate, understanding what strong DevOps competency looks like — and how to surface it in conversation — is essential.

This guide covers the demand for DevOps skills, sample interview questions grouped by topic, and best practices for making strong hiring decisions.

Demand for DevOps Skills

As organizations continue to adopt cloud computing, the demand for professionals who can operate effectively at the intersection of development, operations, and infrastructure has grown steadily. IT engineers, software engineers, and cloud architects are among the roles seeing the most direct benefit from DevOps expertise.

Among the most sought-after technical skills for DevOps practitioners are:

Configuration management: Experience with tools such as Puppet, Chef, and Ansible to automate infrastructure provisioning and maintain consistency across environments.
Containerization and virtualization: Hands-on experience with platforms like Docker, Kubernetes, OpenStack, and cloud providers such as AWS, Azure, or GCP.
Scripting and programming: Proficiency in languages commonly used in automation and tooling, including Python, Ruby, Go, Bash, and Java.
CI/CD pipeline management: Experience designing, building, and maintaining continuous integration and continuous delivery workflows.
Monitoring and observability: Familiarity with monitoring stacks and the ability to design alerting and incident response strategies.

Sample DevOps Interview Questions

The following questions are grouped by topic area. They are useful both for interviewers designing a structured assessment and for candidates preparing their responses.

Understanding of DevOps

What is your understanding of DevOps?

This foundational question assesses whether the candidate understands DevOps as more than a toolchain — it is a cultural and organizational approach that improves collaboration between software development and IT operations teams. A strong answer will go beyond listing tools and describe how DevOps shortens feedback loops, reduces deployment risk, and aligns engineering work with business outcomes.

Why does DevOps matter, and what problems does it solve?

This question probes the candidate’s ability to articulate the business value of DevOps. Look for answers that address both the technical benefits — such as faster, more reliable deployments and reduced mean time to recovery — and the cultural benefits, including breaking down silos between development and operations teams. Candidates who have lived through a DevOps transformation will often have concrete examples to draw on.

Agile Development

How are DevOps and Agile methodology related?

DevOps and Agile are complementary but distinct. Agile focuses on iterative software development in short cycles; DevOps extends this thinking into the deployment and operations phases. A well-prepared candidate will explain that DevOps is often seen as the natural operational companion to Agile development — both share a commitment to rapid feedback, collaboration, and continuous improvement. They should also be able to distinguish where each practice applies and how they interact in practice.

What is the role of a DevOps engineer within an Agile team?

This question uncovers whether the candidate understands their specific responsibilities in a cross-functional team. Key functions include maintaining CI/CD pipelines, enabling automated testing, managing infrastructure as code, and supporting rapid, reliable delivery of each sprint’s output. Strong candidates will also highlight the importance of acting as a bridge between developers and operations — attending standups, participating in sprint planning, and helping the team understand infrastructure constraints early.

Software Deployment

Describe your experience with software deployment pipelines.

This open-ended question lets the candidate demonstrate hands-on experience with deployment automation. Listen for familiarity with tools like Jenkins, GitLab CI, GitHub Actions, CircleCI, or ArgoCD, and whether the candidate can describe a real pipeline they have designed or maintained. The best answers will touch on how they handled environment parity, secrets management, and rollback strategies.

How have you handled a failed deployment in production?

Deployment failures are inevitable. This behavioral question assesses how the candidate responds under pressure. Look for a structured approach: detecting the failure quickly, deciding whether to roll back or hotfix, communicating clearly with stakeholders, restoring service, and conducting a blameless post-mortem to prevent recurrence. Candidates who have never experienced a production failure may not have been working at sufficient scale or ownership level.

How do you detect failures in production?

This question directly tests the candidate’s approach to monitoring and observability. A strong answer will describe a layered strategy: infrastructure metrics (CPU, memory, disk), application-level metrics (error rates, latency, throughput), log aggregation, distributed tracing, and alerting thresholds. The candidate should be able to name tools they have used — such as Prometheus, Grafana, Datadog, the ELK stack, or similar — and explain how they configured meaningful alerts rather than alert noise.

How can configuration management tools support a DevOps practice?

Configuration management tools like Ansible, Chef, Puppet, and SaltStack allow teams to define infrastructure state as code, apply it consistently across environments, and audit drift. A solid candidate will be able to compare at least two tools, describe when they would choose one over another (for example, agentless versus agent-based models), and explain how these tools integrate into a broader CI/CD pipeline or infrastructure-as-code workflow alongside tools like Terraform.

Why Asking the Right Questions Matters

Structured, topic-focused interview questions serve a practical purpose beyond filtering candidates. They allow you to:

Map candidate strengths against specific gaps in your existing team — one candidate may be stronger on the infrastructure and operations side, while another brings deep CI/CD or development expertise.
Objectively compare candidates using consistent criteria rather than gut feel.
Assess depth of experience versus surface-level familiarity with buzzwords.
Determine whether a candidate understands the full software delivery lifecycle, not just their slice of it.
Gauge a candidate’s ability to collaborate across team boundaries — a critical soft skill in DevOps environments.

DevOps roles vary significantly between organizations. A startup may need a generalist who can own the entire pipeline, while an enterprise may need a specialist who fits into a larger platform engineering team. Tailor your questions accordingly.

Best Practices for Hiring DevOps Engineers

Effective DevOps hiring goes beyond the interview itself. Consider the following principles when building your hiring process and onboarding approach.

Create an environment where good engineers can do their best work

Hiring exceptional engineers is only half the equation. If your organization’s culture, processes, or tooling prevent those engineers from exercising their expertise, the talent advantage disappears quickly. Before worrying about hiring the best people on the market, evaluate whether your current environment supports autonomy, fast feedback, and psychological safety. Engineers who are trusted to make decisions and learn from failures will outperform those who are micromanaged regardless of their raw skill level.

Prioritize problem-solving ability and initiative over trivia

It is tempting to use technical screening questions as a proxy for competence — asking candidates to recite specific port numbers, obscure configuration syntax, or niche protocol details. Resist this temptation. Specific technical details can be looked up in seconds; what cannot be easily taught is the drive to identify a problem, design a solution, and see it through to completion. Focus your interview process on how candidates approach ambiguous problems, how they communicate during incidents, and how they learn from mistakes.

Assess cultural and collaborative fit alongside technical skill

DevOps is fundamentally about collaboration. A technically brilliant engineer who refuses to share knowledge, dismisses feedback, or works exclusively in silos can actively damage a DevOps culture. During interviews, pay attention to how candidates describe past team dynamics, how they talk about colleagues in other disciplines, and whether they show genuine curiosity about the problems your team is trying to solve.

Consider practical assessments

Where appropriate, supplement verbal interview questions with a practical exercise — such as reviewing a Dockerfile or a CI pipeline configuration and asking the candidate to identify issues and suggest improvements. Keep take-home assessments short and respectful of the candidate’s time, and always provide feedback regardless of outcome.

Additional Resources

For further reading on DevOps interviewing and hiring, the following articles on this site may be useful:

6 Steps to Hiring and Retaining Great DevOps Talent

anil — Fri, 14 Apr 2017 14:26:34 +0000

This article is based on a piece originally published on news.sys-con.com (April 2017). The core advice remains highly relevant to DevOps hiring today.

Why DevOps Hiring Is Different

Like Cloud and Agile before it, DevOps is a concept that key stakeholders often oversell or misunderstand. It cannot be reduced to a one-line job description—and nowhere does that complexity show up faster than in the hiring process.

Unless your entire hiring process is aligned around a shared, honest understanding of what DevOps means in your organisation, you risk attracting the wrong candidates and losing the right ones. Everyone involved in the hiring decision needs to agree on the outcome they are working toward.

The Large-Company Problem

Smaller organisations often benefit from a personal, informal hiring process where the hiring manager has daily contact with candidates and can involve the wider team. Larger companies rarely have that flexibility. HR teams and recruiters that excel at sourcing traditional delivery engineers may not yet have a strong grasp of the DevOps skill set—technical breadth, cultural fit, and an appetite for continuous improvement all matter alongside domain expertise.

Improving your chances of finding the right people requires more hands-on involvement from engineering leaders than has traditionally been the norm. It may also mean setting cultural expectations that differ from the broader business culture HR teams are used to representing.

Be Honest With Candidates

Where traditional hiring tends to lead with a company’s strengths, DevOps hiring demands a more candid conversation. That means sharing the challenges ahead—the cultural gaps, the tooling debt, the organisational friction—alongside the aspirations. This transparency feels uncomfortable, but it produces better outcomes:

Candidates who are genuinely excited by the challenge will self-select in.
Candidates who expect to join a fully mature DevOps organisation will self-select out—saving everyone time.
The people you hire will have realistic expectations, reducing early attrition.

Six Steps to Better DevOps Hiring and Retention

1. Educate and Inform the Hiring Channel

Be very specific when briefing recruiters—internal or external. Go beyond a list of tools and certifications. Explain the cultural direction you are heading, the maturity level of your current DevOps practice, and the role you expect the new hire to play in evolving it. A candidate hired as a change agent who arrives to find the transformation is already complete—or who arrives expecting it to be—is a poor fit that wastes everyone’s time and money.

2. Increase Your Internal Hiring Capability

Replace or supplement the traditional one-on-one interview format with collaborative discussions that involve two or three team members from the first meeting. This approach has several advantages:

It immediately signals the collaborative culture you want to build.
It distributes assessment across more experienced eyes.
It surfaces real-world experience stories that a single interviewer might miss.

Ensure that whoever leads these discussions understands and actively supports the DevOps direction the organisation is taking.

3. Reduce the Process Friction

Day one is the first real test of your culture. If a new hire arrives and spends their first day battling online compliance forms, sitting through generic inductions, and waiting days for a laptop and credentials, they immediately see a gap between what was promised and what exists. Streamlining onboarding—while keeping appropriate security controls—demonstrates that the organisation can execute on cultural commitments, not just talk about them.

4. Schedule Early Feedback Discussions

Book structured one-to-one check-ins within the first few weeks. These conversations should create space for the new hire to be genuinely candid: What is matching expectations? What is not? What blockers are they encountering? Where appropriate, extend this to a small group discussion to reinforce the message that open communication is a team norm, not just a management talking point. Use what you hear to adjust—and to validate whether your understanding of the current state is accurate.

5. Support Team Builders; Address Team Breakers Early

Any role focused on changing culture will create discomfort for some existing team members—that is expected and, to a degree, healthy. But there is a meaningful difference between productive disruption that moves the organisation forward and destructive behaviour that demoralises the team without delivering progress. Managers need to distinguish between the two and act decisively. Failing to address a poor hire during the probation period protects no one and risks driving away high performers who have no tolerance for dysfunction.

6. Follow Through on the Cultural Promise

If you have been honest with candidates about the cultural journey ahead, you have made an implicit commitment. No single hire should be expected to carry a transformation alone. Strong DevOps engineers—the ones you most want to retain—are acutely good at identifying an impossible mission. If they see that leadership is not actively removing obstacles, providing resources, and championing the change, they will leave. They have options. Make sure you are doing your part: empowering, enabling, and visibly backing the change you asked them to help drive.

Closing Thoughts

There are few easy hires in a technical field, and DevOps engineers are no exception. When those same engineers are also expected to influence cultural change, the stakes are even higher. A hiring process that is honest, collaborative, and well-supported by leadership is not just a nice-to-have—it is the foundation on which a sustainable DevOps practice is built.

Container Adoption, Security, and the Future: Liz Rice in Conversation with AppDynamics

anil — Tue, 11 Apr 2017 06:52:51 +0000

This article is based on an interview originally published on sys-con.com, conducted by AppDynamics ahead of AppD Summit Europe. Liz Rice was a prominent container and cloud-native security expert, known for her work at Aqua Security and her contributions to the CNCF community.

Why Containers Caught On So Quickly

Containerisation technologies existed before Docker, but Docker was the tool that made them genuinely accessible at the command line. That accessibility opened up the benefits of containers to mainstream developers, who quickly discovered how containers could simplify dependency management and eliminate the classic “but it works on my machine” problem.

By packaging an application and its dependencies into a container image, teams can reproduce exactly the same environment on a developer laptop as in production. From there, the community began exploring how well containers fit with microservices architectures and CI/CD pipelines, making it faster and more reliable to build and ship code. That speed of delivery is something virtually every organisation wants from its software teams.

Tips for Starting Your Container Journey

Containers can touch every stage of the software lifecycle — development, continuous delivery, orchestration, security, and site reliability engineering. The breadth of the technology can make adoption feel overwhelming. Rice’s advice: you do not have to do everything at once.

Start by anchoring the effort to a clear business goal. For most organisations, the strategic driver is increasing deployment speed. Containers are most valuable when teams are already working with agile methodologies and shipping code frequently through a CI/CD pipeline.

The path to containerisation looks different in every company — and even in different teams within the same company:

Bottom-up adoption: Individual development teams bring containers into their local workflow to make their own lives easier. Container usage then grows organically across the organisation.
Top-down platform adoption: A platform team builds an orchestrated cluster and offers it to the rest of the business. Software teams are then invited to migrate their workloads into containers so they can run on the shared cluster.

Both approaches are valid. Conference talks, meetups, and case study presentations are a rich source of real-world experience from teams who have been through the journey. Interactive learning platforms such as Katacoda (now archived into the Killercoda platform) offer hands-on Docker and Kubernetes labs that are well suited to engineers just starting out.

Overcoming the Typical Challenges of Container Adoption

Getting Security Leadership On Board

One of the most common blockers is persuading the CISO that containerised deployments are safe. Security leads often ask whether shipping code more frequently increases the organisation’s risk profile. The counter-argument is straightforward: continuous delivery means shipping small, incremental changes. Each individual update carries little additional risk compared to the previous one, and automated controls such as container image scanning can detect known vulnerabilities in dependencies before they reach production.

Microservices also make security reasoning easier. It is far simpler to define and enforce what a small, focused microservice should be able to access — which files, which user IDs, which network endpoints — than it is to reason about access controls across a large monolithic codebase. Container-native security tools can apply those policies at the level of individual containers.

Convincing Technical Leadership

CTOs and engineering directors need confidence that containerised processes can produce well-tested, production-quality software. The case to make is both technical — faster, more reliable delivery pipelines — and cultural: containers and CI/CD encourage an agile mindset and continuous improvement. That said, containers are not universally the right choice. Organisations with heavily plan-driven, waterfall delivery processes may find that the cultural shift required is too significant to tackle alongside the technical one.

Progress in Container Security

Container security has matured considerably since the early concerns about shared kernel namespaces and container escape. Improvements in the Linux kernel — such as user namespace support — directly addressed some of those initial concerns. Rice points to the ShellShock vulnerability as a useful illustration of how containerisation can actually improve a security posture:

Most microservices don’t need a shell, so you can run them in containers that don’t include the shell. In the event of another ShellShock being discovered, you would still have to worry about patching the host, but you wouldn’t need to change those microservice containers at all. That means less testing and less risk when the patch is applied.

By stripping container images down to only the dependencies a service actually needs, teams reduce the attack surface of each workload. Dedicated container security platforms (such as Aqua Security, where Rice worked at the time of this interview) have helped enterprises build robust security policies for containerised deployments, and Docker’s Enterprise Edition introduced formal testing and certification processes as evidence of that growing maturity.

How Skill Sets Need to Evolve

Containers tend to simplify the developer experience, but they introduce a significant learning curve on the operations side. Key areas where thinking must shift include:

Orchestration: In an orchestrated cluster, you do not control which node runs which workload. Operations teams must move from managing individual machines to reasoning about the cluster as a whole.
Observability: Monitoring, distributed tracing, alerting, and diagnostics tooling is evolving rapidly to handle the dynamic, ephemeral nature of containerised workloads. Traditional host-centric monitoring approaches do not translate directly.
Infrastructure as code: Container-native infrastructure is more automated, less proprietary, and more collaborative than traditional server provisioning. Teams need to adopt infrastructure-as-code practices alongside container adoption.

Where Containers Are Headed

At the time of this interview (2017), Rice observed that the community was moving from experimentation to running containers seriously in production. Container fundamentals were already stabilising, but significant room remained for tooling improvements in CI/CD, monitoring, diagnostics, and programmable infrastructure.

The broader trajectory she described — containers moving from early-adopter curiosity to mainstream infrastructure — has since proven accurate. Kubernetes has become the dominant orchestration platform, observability tooling has matured substantially, and container security has evolved from an afterthought into a dedicated discipline. The foundational advice in this interview — start with a clear business goal, address security concerns with evidence, and invest in team skills — remains relevant for organisations still navigating their container adoption today.

Originally published April 2017. Source: sys-con.com / AppDynamics. Interview subject: Liz Rice, then at Aqua Security.

Continuous Delivery the ObjectStyle Way: A Practitioner Interview

anil — Mon, 27 Feb 2017 05:15:56 +0000

Originally published on DZone. Interview conducted by Alesia Krush with Evgeny Vintik, Team Lead at ObjectStyle.

Background: Why CI Was Needed

Evgeny Vintik came to ObjectStyle having already worked in an environment where Continuous Integration (CI) was standard practice. His previous employer used TeamCity to coordinate work and shipped product updates every week. When he joined ObjectStyle, things looked quite different: releases were irregular, pre-release testing consumed enormous amounts of time, unit tests were sparse and many of those that existed were outdated and effectively useless.

Recognising the gap, Vintik set out to introduce CI as a core part of his team’s daily development routine — a process that took roughly a year and a half to take root.

The Challenges of Changing the Mindset

Introducing CI is as much a cultural challenge as a technical one. Vintik identifies two main obstacles his team faced:

Breaking established habits. Developers accustomed to a particular workflow are naturally reluctant to leave their comfort zone. The good news, Vintik notes, is that CI delivers visible results relatively quickly, which helps build momentum.
Maintaining zero tolerance for failing tests. Once a code-quality standard is agreed upon — specifically, that code must pass all existing tests before it can be merged — it is critical to enforce it without exceptions. Letting a few minor failures slide can snowball rapidly. Each concession makes the next one easier to justify, and before long the team is accumulating technical debt instead of practising Continuous Delivery.

Practical Strategies for Making CI Stick

Enforcing Test Coverage at the Branch Level

To encourage developers to write more unit tests, the team introduced a rule: if test coverage in a feature branch fell below the average coverage in the master branch, the build would fail. This acts as an automatic gate, preventing developers from merging code that does not carry its own weight in tests.

End-to-End Testing: Start Small, Focus on Core Functionality

End-to-end (E2E) tests already existed in some form when Vintik’s team started, but they were run inconsistently and many were outdated. Writing and maintaining E2E tests is time-intensive, so the team adopted a focused strategy: cover the core functionality thoroughly rather than trying to test every minor feature. Vintik observes that E2E tests deliver the greatest return on investment over time — the longer you invest in them, the cheaper they become to maintain and extend.

The overall testing strategy the team settled on can be summarised as:

Unit tests: maximum coverage across the codebase.
End-to-end tests: focused coverage of critical features and user journeys.

Tools the Team Evaluated and Adopted

Choosing the right CI tooling was an iterative process for ObjectStyle:

Atlassian Bamboo (cloud): The team’s first choice. According to Vintik, Atlassian discontinued support for the cloud-hosted version of Bamboo in 2017, which forced a switch.
JetBrains TeamCity: The team migrated to TeamCity and found it a strong fit, particularly for Java projects. It is built with CI in mind and offers a robust free tier for smaller teams.
Bitbucket Pipelines: Vintik recommends this for teams looking for a language- and platform-agnostic option. It requires no dedicated server infrastructure, reducing operational overhead.
GitHub-integrated CI services: For projects hosted in public GitHub repositories, several CI services integrate directly with the platform and are worth evaluating.

The Real Benefits of Continuous Integration

Beyond the industry trend, Vintik articulates concrete, compounding benefits his team experienced:

Introducing changes becomes cheaper over time. The more code that has been written under CI discipline, the easier it is to add new features or refactor existing ones without fear of regressions.
Fewer surprises. Automated tests catch compliance issues immediately, turning what used to be late-stage QA discoveries into routine, low-stakes feedback loops.
Developers can focus on programming. Instead of manually passing tickets back and forth between development and QA, engineers receive fast, automated feedback and can adjust code on the spot.

Vintik offers a memorable analogy: developing software with immediate automated feedback is like using a digital camera instead of film — you no longer have to wait to see whether the shot came out.

How CI Relates to Agile, DevOps, TDD, and Continuous Delivery

These terms are frequently bundled together. Vintik draws useful distinctions:

Agile is a philosophy or methodology. Teams can practise Agile principles without adopting any specific framework (Scrum, Kanban, etc.). CI is what makes frequent Agile releases technically feasible — without it, shipping working software at the pace Agile demands is very difficult.
DevOps is a broader organisational concept, most tangible in large companies shipping mass-market products. At scale — think hundreds of servers receiving multiple deployments per day — a dedicated team or function responsible for releases, version control, and deployment infrastructure becomes essential.
Continuous Delivery is the outcome of sustained investment in CI. Once a team has built up solid test coverage and reliable pipelines, they arrive at a state where releasing software becomes routine rather than an event. It is the reward for the hard work put into CI over time.
TDD (Test-Driven Development) is a coding practice that complements CI by ensuring tests are written before or alongside code rather than as an afterthought, supporting the high coverage levels that CI requires.

Continuous Delivery in a Sentence

Asked to distil Continuous Delivery into a few words, Vintik puts it this way:

In Continuous Delivery, development is a journey, not a destination. The developer’s work is never done. And if it is done, this spells the death of a project.
— Evgeny Vintik, Team Lead, ObjectStyle

Key Takeaways

Cultural resistance is the first barrier to CI adoption — early, visible wins help overcome it.
Zero tolerance for failed tests is not optional; every exception erodes the standard.
Enforce coverage at the branch level to prevent regression without manual policing.
E2E tests are expensive to write but deliver compounding returns; prioritise core functionality.
Tool choice matters less than discipline — TeamCity, Bitbucket Pipelines, and GitHub-integrated services are all viable starting points.
Continuous Delivery is not a tool or a framework; it is the measurable outcome of sustained CI practice.

Why You’ll Never Nail That DevOps Interview (And How to Fix It)

anil — Mon, 02 Jan 2017 16:37:00 +0000

This article is based on an original piece published on VentureBeat. The arguments and examples have been expanded and restructured for a DevOps engineering audience.

The Hiring Problem Nobody Wants to Admit

The DevOps talent shortage is one of the most-discussed problems in the industry. Companies at every scale complain they cannot find the right people. Recruiters blame a thin market. Candidates blame unrealistic job descriptions. Everyone agrees something is broken.

But here is the uncomfortable truth: we cannot even know whether a genuine talent shortage exists, because the hiring process itself is fundamentally broken—on both sides of the table. Recruiters do not know what they are actually looking for, and job seekers, responding rationally to those signals, present themselves in exactly the wrong way.

Adrian Cockcroft, formerly VP of Cloud Architecture at Netflix and later a partner at Battery Ventures, put it well when asked by large enterprises where Netflix found such great engineers. His answer: “We hired the same people you hired. We just got out of their way.” The talent exists. The environment and the selection process are what fail.

Andrew Clay Shafer, a well-known figure in the DevOps community, made a similar argument in a 2013 talk titled “There Is No Talent Shortage,” asserting that the people organisations need are already in front of them—they are simply looking for the wrong signals.

How Operations Engineering Changed

To understand why hiring is so confused, it helps to understand how Operations Engineering evolved. A decade ago, system administrators typically owned a specific subsystem: networking, storage, databases, or application servers. Roles were narrower and more specialised.

The rise of cloud platforms, on-demand infrastructure, and continuous delivery changed that entirely. Small ops teams—sometimes four to six people—now own the full production stack: infrastructure design, monitoring pipelines, deployment automation, configuration management, incident response, and security posture. Teams of five are doing work that once required tens of specialists.

This is genuinely new territory. There is no established academic curriculum for it. There is no certification that proves competence across the full breadth of the role. The skills required are still being discovered in real organisations, in production, under pressure.

What the “DevOps Engineer” Job Description Actually Looks Like

The HR response to this new reality has been to compress all of that complexity into a single job description. Here is a representative example drawn from a real LinkedIn posting (reproduced from the original source article):

Requirements: B.Sc. from a four-year university; at least 10 years of infrastructure development experience; 3+ years of AWS experience; 5–7 years of general DevOps/WS/DB/infrastructure experience; practical experience with EC2, Beanstalk, ELB, Route53, S3, CloudFront, SNS, SWF, SQS, RDS, DynamoDB, ElastiCache, IAM, and CloudFormation; familiarity with SOA principles; SDLC experience using Chef, Puppet, and CloudFormation; exposure to Chaos Monkey and Asgard; hands-on experience with monitoring, intrusion detection, and vulnerability assessment tools; general scripting wizardry.
Additional preferred skills: Familiarity with iTunes/App Store fundamentals; SQL and NoSQL experience; experience designing access control and authentication systems; strong verbal and written communication skills.

The person described in that job description does not exist—or if they do, they are already employed at a company that can match Google or Amazon compensation and offer them problems worth solving. Posting this description and waiting for that candidate to apply is not a hiring strategy. It is wishful thinking dressed up as a requirement.

Four Mistakes Breaking the DevOps Interview Process

Mistake 1: Job Descriptions Describe People Who Do Not Exist

The “DevOps Engineer” title has become a catch-all for CI/CD, configuration management, cloud architecture, automation, security, and infrastructure design. Organisations reason that if they can find one person who covers all of this, they save the cost of hiring several specialists. That reasoning is understandable. It is also wrong.

Modern Operations Engineering is a genuinely new profession. Developing deep competence across infrastructure design, observability, deployment pipelines, and security takes years—and the field itself is still evolving. Demanding that a candidate have achieved that depth with fewer than four years of the role even existing is incoherent.

The “years of experience” metric compounds the problem. The assumption that four years of experience outweighs two years is not supported by how people actually develop. Someone can be mediocre for five years while another person becomes genuinely skilled in two, depending on the environments they worked in, the problems they were given, and how intentionally they learned.

University degrees present a similar issue. Academic curricula move far slower than the technology industry. There is no university course that teaches you how to design a scalable monitoring pipeline, manage a Kubernetes cluster under load, or build a zero-downtime deployment strategy for a stateful service. These skills are learned in production, not in lectures.

Mistake 2: Interviewers Ask the Wrong Questions

Technical interviews for operations and DevOps roles frequently test memorisation of specifics: exact port numbers, configuration file syntax, command-line flags. These are the wrong things to test.

An experienced engineer who has not configured a GRE tunnel in Junos for two years will not remember the exact syntax—and that is fine. They know the concept, they know where to look, and they know how to validate whether it worked. Trivia-style questions eliminate strong generalist engineers while potentially passing candidates who have recently drilled specific technologies without understanding the underlying principles.

A better interview question: “Here is a system. Design the most secure pipeline for sending requests from an application server to a database. Walk me through your research process, the trade-offs you considered, and how you would validate the solution.” That question reveals how someone actually thinks—which is what you need to know.

Mistake 3: Candidates List Tools Instead of Demonstrating Capability

Consider a typical DevOps resume career summary:

System Administration: GNU/Linux (Debian, Ubuntu, Arch, CentOS, RHEL)
Virtualisation: LXC, KVM, OpenVZ, AWS
Configuration Management: Ansible, Chef (learning), Make
Languages: Python, Ruby (learning), Shell, C, C++, Java
Databases: OpenLDAP, MySQL, MariaDB
Version Control: Git, Subversion, Bazaar
Monitoring: Nagios, ntop, SSH, DHCP, DNS

Even assuming every item on that list represents genuine experience, this resume tells you almost nothing useful. It does not tell you whether this person can identify that a problem exists, let alone solve it. It does not tell you how they approach unfamiliar systems, how they handle failure under pressure, or what they have actually built.

Compare these two resume lines:

Weak: “Experience with Nagios, Statsd, Graphite, Logstash, Elasticsearch, and Kibana.”
Strong: “Researches and implements monitoring solutions for complex distributed systems. Built an observability pipeline at [Company] that reduced mean time to detection from 45 minutes to under 5 minutes.”

Or these:

Weak: “Four years of experience with Ruby, Python, AWK, Shell, C, C++, Java, and JavaScript.”
Strong: “Works primarily in Ruby and Python. Picks up new languages when the problem calls for it—recently learned Go to contribute to [open source project]. Example: [link to repository].”

The second version in each case gives an interviewer something concrete to follow up on. It signals intellectual honesty and genuine depth rather than keyword optimisation.

Mistake 4: Candidates Talk About the Wrong Things in Interviews

When a candidate is asked an open-ended architecture question, the temptation is to respond by listing the scale of systems managed or the variety of platforms encountered. Neither of those things answers the question.

The number of servers you have managed does not predict your ability to design a secure request pipeline. The range of IaaS platforms you have touched does not demonstrate that you can reason about trade-offs, document your thinking, or evaluate whether your solution actually worked.

What interviewers should be looking for—and what candidates should be demonstrating—is the process of problem-solving: how you identify the problem, how you research solutions, how you evaluate trade-offs, how you test your assumptions, and how you communicate what you found.

What Good DevOps Hiring Looks Like

For Recruiters and Hiring Managers

Define the actual problem the hire needs to solve. What is broken or missing in your organisation right now? Write a job description around that problem, not around an idealised skill checklist.

Look for passion through digital footprints. Stack Overflow and Stack Exchange profiles, GitHub repositories, conference talk proposals, meetup attendance, mailing list participation, and open-source contributions are all stronger signals than years of experience.

Test problem-solving, not memorisation. Give candidates a realistic scenario, give them access to documentation, and watch how they work through it. That is what they will actually do on the job.

Reconsider the degree requirement. There is no formal academic path to modern operations engineering. Requiring a degree in computer science eliminates a significant portion of the people who built the skills you need through self-directed learning and real-world experience.

Separate “must have” from “nice to have” honestly. If your job description lists twenty requirements, accept that a candidate who meets twelve of them well may outperform one who nominally meets all twenty.

For Job Seekers

Lead with outcomes, not tools. What did you build? What broke and how did you fix it? What improved as a result of your work? Quantify where you can.

Show your thinking publicly. A blog post walking through a problem you solved, a GitHub repo with a project you care about, or an answer on Stack Exchange that helped someone—these are worth more than any list of technologies on a resume.

Be honest about depth versus breadth. Know two or three things well and say so clearly. Acknowledge what you are learning. Interviewers value intellectual honesty far more than an implausibly comprehensive skill list.

Practice architecture discussions, not trivia. Prepare to talk about how you approach problems, not just which tools you have used. Be ready to whiteboard a system, explain your reasoning, and say “I don’t know, but here is how I would find out.”

The Real Issue With “DevOps Engineer” as a Job Title

The term “DevOps Engineer” is itself part of the problem—not because the words are wrong, but because they have come to represent an impossible combination of skills rather than a genuine role. DevOps, as a concept, describes a culture of collaboration between development and operations teams, supported by automation and shared responsibility for production systems. It is not a job title. It is not a person.

Using it as a job title creates a feedback loop: recruiters post “DevOps Engineer” because they have heard the term and associate it with everything they need; candidates put “DevOps” on their resumes because they see it in job descriptions; and neither side is communicating anything precise. The term becomes a signal of buzzword compliance rather than genuine capability.

This does not mean the term should be banned from job descriptions. It means that when you write or read one, you should look past the label to the actual work being described—and evaluate candidates accordingly.

Conclusion

There may or may not be a DevOps talent shortage. We genuinely cannot tell, because the process for identifying and evaluating talent is too broken to produce a reliable signal. Recruiters are filtering for unicorns. Candidates are optimising for keyword matches. And real engineers with genuine problem-solving ability are falling through the gap between the two.

The fix is not complicated, but it does require both sides to be more honest about what matters. Organisations need to define the problems they need solved and evaluate candidates on their ability to think, learn, and deliver—not on whether their resume contains the right keywords. Candidates need to demonstrate the depth of their thinking, not the breadth of their tool exposure.

The talent is there. We are just not looking for it correctly.

interview – Best DevOps

Top 30 DevSecOps Interview Questions with Answers

1. What is DevSecOps?

2. What are the benefits of DevSecOps?

3. What are the challenges of DevSecOps?

4. What are the different stages of the DevSecOps lifecycle?

5. What are the different roles in DevSecOps?

6. What are the skills required for a DevSecOps engineer?

7. What are the career opportunities for DevSecOps engineers?

8. How does DevSecOps handle vulnerability assessment and management?

9. What role does static application security testing (SAST) play in DevSecOps?

10. How can dynamic application security testing (DAST) improve security in DevSecOps?

11. Describe the purpose of “container security” in DevSecOps.

12. How does DevSecOps handle secrets and credentials management?

13. What is “shift-right” security testing in DevSecOps?

14. How does DevSecOps handle compliance and regulatory requirements?

15. Explain the concept of “continuous monitoring” in DevSecOps.

16. What are some common security challenges faced in a DevSecOps environment?

17. How does DevSecOps support the “least privilege” principle?

18. Describe the role of “security champions” in DevSecOps.

19. What is “continuous security testing” in DevSecOps?

20. How can DevSecOps help with incident response and recovery?

21. What is Vagrant?

22. What is the use of PTR in DNS?

23. What is Chef?

24. What are the prerequisites for the implementation of DevOps?

At least one Version Control Software

25. Name some best practices which should be followed for DevOps success.

26. Explain SubGIt tool

27. Name some important network monitoring tools

28. Whether your video card can run Unity how would you know?

29. Explain how to enable startup sound in Ubuntu?

30. What is the quickest way to open an Ubuntu terminal in a particular directory?

Top 30 MLOps Interview Questions with Answers

1. What is MLOps?

2. What are the benefits of MLOps?

3. What are the challenges of MLOps?

4. What are the most popular MLOps tools?

5. What are the different stages of the MLOps lifecycle?

6. What are the different roles in MLOps?

7. What are the skills required for an MLOps engineer?

8. What are the career opportunities for MLOps engineers?

9. What is “feature engineering” in the context of MLOps?

10. How does MLOps address the challenges of model interpretability and explainability?

11. Describe the concept of “model monitoring” in MLOps.

12. What is “Continuous Integration and Continuous Deployment” (CI/CD) in MLOps?

13. How does MLOps handle data privacy and security concerns?

14. Explain the role of containerization in MLOps.

15. What is the significance of model retraining in MLOps?

16. How can MLOps help manage the technical debt associated with machine learning projects?

17. Describe the concept of “model versioning” in MLOps.

18. What is the role of MLOps in enabling collaboration between data scientists and developers?

19. How does MLOps handle model deployment and scaling?

20. Explain the concept of “Model Governance” in MLOps.

21. How does MLOps address the reproducibility challenge in machine learning?

22. What is the role of A/B testing in MLOps?

23. How does MLOps contribute to model deployment consistency?

24. Explain the concept of “experiment tracking” in MLOps.

25. How can MLOps enhance model reusability across different projects?

26. Describe the role of “model explainability” in MLOps.

27. What is “Hyperparameter Tuning” in the context of MLOps?

28. How do MLOps handle challenges related to data drift and concept drift?

29. What are some challenges organizations might face when implementing MLOps practices?

30. How does MLOps support the deployment of machine learning models in edge computing scenarios?

Top 30 AiOps Interview Questions with Answers

1. What is AIOps?

2. What are the benefits of AIOps?

3. What are the challenges of AIOps?

4. What are the different types of AIOps solutions?

5. What are the most popular AIOps tools?

6. What are the different stages of AIOps maturity?

7. What are the different use cases for AIOps?

8. What are the skills required for an AIOps engineer?

9. What are the career opportunities for AIOps engineers?

10. How can AIOps improve capacity planning?

11. Explain the concept of “proactive monitoring” in AIOps.

12. What is “self-healing” in the context of AIOps?

13. How does AIOps contribute to business agility?

14. What is the importance of real-time analytics in AIOps?

15. How does AIOps handle data from various sources and formats?