harvey DevOps Engineer Interview: Questions & Prep (2026)

Harvey is an AI-powered legal technology company building tools for lawyers and law firms. As a platform handling sensitive legal documents, the infrastructure demands centre on reliability, data security, and performance. Harvey currently has 367 open roles, making them one of the most active hirers in the AI tech space right now.

Candidates report that the interview process typically includes a recruiter call, a technical screening, one or two deeper rounds on cloud and CI/CD topics, and a final system design or behavioural conversation. Round order and count can vary by team, so confirm the structure with your recruiter upfront.

The wider DevOps market across India has 811 open roles as of July 2026. Bangalore leads with 187 openings, followed by Delhi (40), Pune (37), Hyderabad (28), Chennai (13), and Mumbai (11). Salary bands across experience levels:

With 367 roles open, Harvey is clearly in an active growth phase. This guide covers what their interviewers commonly focus on and how to walk in prepared.

Based on Harvey's product domain and patterns candidates report from AI-first tech companies, these are the questions that come up most often:

1. Walk us through a CI/CD pipeline you designed or owned end to end. What tools did you choose and why?
2. Harvey handles sensitive legal documents. How do you approach secrets management and access control across environments?
3. Describe your hands-on experience with Kubernetes. How have you handled scaling, rolling upgrades, or unexpected pod failures in production?
4. How would you design a zero-downtime deployment strategy for a critical API serving real-time users?
5. Tell us about a production incident you personally owned. How did you detect it, contain it, and prevent it from recurring?
6. What is your approach to infrastructure as code? How do you manage state, handle drift, and structure modules for reuse?
7. AI workloads can be compute-heavy. How have you approached cloud cost optimisation in a previous role?
8. Walk us through your ideal monitoring and alerting setup. How do you avoid alert fatigue while keeping on-call engineers well-informed?
9. How do you embed security into a DevOps pipeline? What does a DevSecOps workflow look like for you day to day?
10. Describe a time you worked closely with software engineers to unblock a release or improve developer velocity.
11. How do you handle database schema migrations in production without taking downtime?
12. If you joined Harvey and found that deployments were manual and error-prone, what would your plan for the first few months look like?

Q: Tell us about a production incident you personally owned.

*Situation:* My team ran a multi-tenant SaaS platform on AWS. One evening, the core API began returning errors for a portion of users without any obvious trigger.

*Task:* As the on-call engineer, I was responsible for identifying the root cause, restoring service quickly, and keeping stakeholders informed throughout.

*Action:* I opened our observability dashboards and traced the spike to database connection timeouts on our RDS instance. I scaled up the instance class, added a read replica for read traffic, and pushed a config change to lower the connection pool ceiling on the application side. Throughout the incident I posted regular updates in the engineering Slack channel and drafted a short status note for the customer success team so they could respond to user queries.

*Result:* Full service was restored within the same hour. Our post-mortem produced a new CloudWatch alarm for connection pool saturation and a runbook that any on-call engineer could follow without escalating.

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Q: Walk us through a CI/CD pipeline you designed or owned end to end.

*Situation:* At my previous company, production releases happened manually over SSH, only on Friday afternoons, and frequently caused weekend incidents the team had to handle on their own time.

*Task:* I proposed and led the move to a fully automated deployment pipeline so the team could ship confidently on any day of the week.

*Action:* I built a GitHub Actions CI pipeline that ran linting, unit tests, and Docker image builds on every pull request. For continuous delivery, I introduced ArgoCD with a GitOps model: merging to main triggered an automatic deploy to staging, while production required an explicit approval step. I integrated container image scanning and Vault-based secrets injection so that no credentials ever lived in environment variables or version control.

*Result:* The team moved from one nerve-wracking manual release per week to multiple confident deployments per week. The Friday-night release ritual was eliminated, and new engineers could understand the full release process by reading the pipeline config.

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Q: How do you handle database schema migrations in production without taking downtime?

*Situation:* Our product team needed to add and rename several columns on a high-traffic PostgreSQL table, but our service ran continuously and we had no option for a maintenance window.

*Task:* I had to design a migration strategy that kept the application live and did not break existing queries at any point during the process.

*Action:* I used an expand-contract pattern across three sequential releases. The first release added the new columns without removing the old ones, and updated the application to write to both. The second release migrated existing data in small batches using a background job, keeping lock contention low. The third release removed the old columns once all data was migrated and all reads had switched to the new schema. Each release was independently deployable and could be rolled back without data loss.

*Result:* The migration completed without a single user-facing error or service interruption. The pattern became our standard approach for all future schema changes on live tables.

STAR for behavioural questions. Every behavioural question has the same structure underneath: what happened, what your job was, what you did, and what the outcome was. Answer in that order every time. Keep Situation and Task tight, one or two sentences each. Spend most of your words on Action, because that is what the interviewer is actually evaluating.

Diagnose, fix, then prevent (for incident questions). Incident questions test whether you think systemically. A weak answer describes the fix. A strong answer covers how you detected the problem, how you contained it, how you fixed the root cause, and what you put in place to prevent recurrence. This four-part flow is easy for interviewers to follow and score.

Trade-offs over tools (for design and architecture questions). Harvey interviewers typically care more about why you chose a tool than which tool you chose. When you mention a tool, add one sentence on what problem it solved and what you gave up to use it. For example: 'We chose Terraform because state management and plan previews were essential for our team, though we accepted the overhead of managing remote state ourselves.'

Connect cost to business outcomes. When discussing cloud cost, link your decision to something the business cared about. 'We moved batch jobs to spot instances' is weaker than 'We moved batch jobs to spot instances because the workload was fault-tolerant, and the savings funded additional staging environments that sped up the whole engineering team.'

Ownership mindset. Harvey interviewers want to see that you treat infrastructure as your product. Candidates who say 'I was asked to fix the pipeline' land weaker than those who say 'I noticed the pipeline was a bottleneck, proposed a solution, and drove it to completion without being told to.'

Security awareness without being prompted. Because Harvey handles privileged legal information, security is not a topic saved for the end of the interview. It is expected to surface naturally in how you describe your normal work. Mention secrets management, least-privilege IAM, audit logging, and image scanning as defaults, not as afterthoughts.

Clear communication under pressure. Incident questions reveal how you communicated with your team and stakeholders during a crisis, not just which commands you ran. Interviewers notice whether you kept others informed and documented what was happening in real time.

Comfort with ambiguity. Harvey is a high-growth company where processes and infrastructure are still maturing. Candidates who describe how they have operated in incomplete or rapidly changing environments tend to resonate more than those who only describe perfectly scoped, well-documented work.

Depth over breadth. Being able to go deep on two or three technologies is more valuable than listing every tool you have ever touched. If you mention Kubernetes, expect follow-up questions about RBAC, resource limits, or network policies.

Week 1: Core technical review. Revisit the fundamentals most likely relevant to Harvey's stack: Kubernetes (deployments, services, ingress, RBAC, resource limits), Terraform (state management, modules, workspaces, drift detection), and a CI/CD tool you know deeply such as GitHub Actions, ArgoCD, or Jenkins. For each topic, write down at least one real story from your own experience before moving on.

Week 2: System design practice. Practise designing out loud: a deployment pipeline for a microservices application, a monitoring and alerting architecture, and a zero-downtime database migration strategy. Time yourself. The goal is to structure your thinking before you enter the interview room, not to memorise a single correct answer.

Week 3: Behavioural preparation. Write down five to eight STAR stories from your career. Cover: a production incident you owned, a process you improved from scratch, a time you influenced a technical decision, and a time something you built failed and what you learned. Practise each story until you can tell it clearly in under two minutes.

Before every round. Read Harvey's current job descriptions carefully to spot technologies or priorities they emphasise. Prepare specific questions for your interviewer that show genuine curiosity about the team's current challenges and how success is measured in the role.

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Describing what the tool does instead of what you did. Saying 'Kubernetes handles container orchestration' tells the interviewer nothing about your skill. Put yourself in every sentence: 'I configured the Kubernetes cluster, wrote the RBAC policies for the team, and set up horizontal pod autoscaling based on both CPU and a custom Prometheus metric.'

Skipping the prevention step in incident stories. Many candidates describe the fix but forget what they put in place to prevent recurrence. Harvey cares about reliability, and the prevention step often separates a strong answer from an average one.

Treating security as an afterthought. Finishing a design answer with 'and of course we would think about security later' is a red flag for a company handling sensitive legal data. Weave security considerations throughout your technical answers from the start.

Vague outcomes. 'The team was happy' or 'things improved' does not help an interviewer assess impact. Be specific even without exact numbers: 'deployments that used to take the team an afternoon were fully automated and repeatable' is far more compelling.

Not asking questions. Candidates who ask nothing at the end signal low preparation or low interest. Prepare at least two thoughtful questions for each round, focused on the team's current challenges or how they measure system reliability.

Over-engineering design answers. A clear, well-reasoned simple architecture beats a complicated one you cannot fully defend. Harvey interviewers typically reward sound thinking and honest trade-off analysis over the longest list of services.