openevidence DevOps Engineer Interview: Questions, Experience & Prep (2026)

OpenEvidence is an AI-powered clinical decision-support platform that gives doctors fast, evidence-based answers at the point of care. It is a venture-backed startup that processes medical queries at low latency, making reliability and infrastructure quality central to the product. The company currently has 11 open roles, signalling active growth in its engineering team.

As a DevOps Engineer at OpenEvidence, you are expected to own cloud infrastructure, CI/CD pipelines, observability stacks, and security controls. Because the platform serves healthcare professionals, candidates report that interviewers probe both engineering depth (Kubernetes, Terraform, AWS or GCP) and awareness of compliance-adjacent topics like audit logging and secrets management. The interview process typically includes a recruiter screen, a technical phone screen, and one or more system-design or take-home rounds, though the exact format varies.

Across experience levels, DevOps roles in India range from 6-12 LPA (entry, 0-2 years) to 45-70+ LPA for Lead/Staff positions, based on knok jobradar data.

These questions come up frequently in DevOps interviews at companies like OpenEvidence, based on candidate reports and the nature of the product:

1. Walk me through a CI/CD pipeline you designed end to end. What tradeoffs did you make?
2. OpenEvidence handles real-time medical queries. How would you architect a system to keep API latency low and uptime high?
3. Describe your experience with Kubernetes. How do you handle pod autoscaling and resource limits in production?
4. How do you manage secrets and credentials securely across multiple environments?
5. The product is AI-heavy. Have you built or maintained infrastructure for GPU workloads or model inference pipelines?
6. How would you set up observability (logs, metrics, traces) for a new microservice from scratch?
7. Tell me about a production incident you owned. How did you handle the response and what changed afterward?
8. How do you approach infrastructure as code? Describe a Terraform or Pulumi module you wrote.
9. How would you handle a zero-downtime database migration for a high-traffic service?
10. We are a startup. How do you balance moving fast with keeping infrastructure stable and auditable?
11. What IAM access control model do you prefer for cloud environments, and why?
12. Describe a time you reduced infrastructure costs without hurting reliability or developer experience.

Q: Walk me through a CI/CD pipeline you designed end to end.

*Situation:* My previous team was deploying a monolith manually using shared SSH credentials. Deployments were slow and introduced frequent rollbacks.

*Task:* I was asked to modernise the delivery pipeline for a high-traffic Node.js API.

*Action:* I moved the codebase to GitHub Actions and added lint, unit test, and security scanning stages. I containerised the app and pushed images to ECR. I wrote Terraform modules to manage ECS task definitions and auto-scaling policies, and introduced environment promotion gates so no code moved to production without passing staging.

*Result:* Deployment time dropped to under 10 minutes. Rollbacks became automated via ECS deployment circuit breakers, and the team shipped more frequently and with more confidence.

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Q: Tell me about a production incident you owned.

*Situation:* Our main API was returning errors for a significant share of requests after a routine certificate rotation.

*Task:* I was the on-call engineer and needed to identify and fix the issue quickly.

*Action:* I checked our monitoring dashboards and saw elevated errors tied to one service. I traced the issue to a misconfigured secret ARN in the ECS task definition that was still referencing the old certificate. I updated the ARN, forced a new deployment, and added a runbook for certificate rotation to our internal wiki.

*Result:* Errors dropped to baseline within minutes. The post-mortem identified a gap in our change-management checklist, which we updated to include secret ARN verification after any certificate rotation.

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Q: How have you reduced infrastructure costs without hurting reliability?

*Situation:* Our cloud bill had grown steadily and leadership flagged it ahead of a funding review.

*Task:* I was asked to audit spending and propose cuts that would not affect availability.

*Action:* I used AWS Cost Explorer and Trusted Advisor to identify over-provisioned RDS instances, idle NAT Gateways in dev environments, and unattached EBS volumes. I right-sized the RDS instances during a low-traffic window, deleted unused resources, and set up budget alerts to catch runaway spend early.

*Result:* Monthly cloud spend dropped noticeably with zero production incidents. We also introduced a monthly cost-review ritual so the savings persisted.

For behavioral questions: Use STAR (Situation, Task, Action, Result). Keep Situation and Task brief (one or two sentences each) and spend most of your time on Action and Result. Quantify results using real numbers from your own experience.

For system design questions: Start by clarifying constraints (scale, latency requirements, budget). Walk through components in order: compute, storage, networking, observability, and security. Candidates report that OpenEvidence interviewers care about how you handle failure scenarios, not just the happy path.

For incident questions: Interviewers typically want to see that you moved quickly to mitigate, communicated clearly, and followed up with a lasting fix. Avoid framing incidents as someone else's fault.

For cost and efficiency questions: Show that you understand the tradeoff between cost and reliability. Mention tools you actually used (Cost Explorer, Infracost, Kubecost) rather than speaking in abstractions.

Ownership over outcomes. OpenEvidence is a startup, so they typically look for engineers who treat infrastructure as a product, not just a support function. Candidates report that interviewers respond well to examples where you proactively identified and fixed a risk rather than waiting to be asked.

Cloud-native depth. Expect detailed questions on Kubernetes, Terraform, and at least one major cloud provider. Surface-level answers tend to lead to follow-up probes that dig deeper.

Security and compliance awareness. Because the platform is used in healthcare contexts, interviewers appear to value engineers who can speak to IAM least-privilege models, audit logging, and secrets management without being prompted.

AI infrastructure familiarity. OpenEvidence is built on large language models. Even if you have not managed GPU clusters, knowing how model inference pipelines are deployed (via containers, auto-scaling, load balancing) shows relevant context.

Communication and incident culture. Strong candidates tell clear incident stories: what broke, what they did, and what changed after. Blameless post-mortem language is a positive signal.

Use the week before your interview to cover the following areas:

Candidates report that OpenEvidence interviews typically value concise, practical answers over long theoretical explanations. Practise saying each STAR story in under two minutes. If you want to track down open roles while you prepare, knok checks 150+ job sites nightly, applies to jobs matching your resume, and messages HR for you.

Being vague about tools. Saying 'I used Kubernetes' without explaining your specific setup (cluster size, how you handled scaling, networking) signals surface-level experience. Name the actual tools and configurations you used.

Skipping the why. Interviewers at product-focused startups often ask why you chose a particular approach. If you picked ECS over Kubernetes, or Pulumi over Terraform, be ready to explain the tradeoff in that specific context.

Treating security as an afterthought. Many candidates describe their architecture and only mention security at the end when prompted. At a company handling medical data, weaving security into your design answer from the start is a stronger signal.

Underestimating the startup context. Describing a process that requires a large specialist team or a months-long rollout does not fit a startup hiring context. Show that you can build and iterate quickly with a small team.

Overloading the incident story. Some candidates spend too long on the technical details of what broke and not enough on how they communicated during the incident and what changed afterward. Interviewers want the full arc, not just the debugging.

Not preparing questions for them. Leaving the 'do you have questions?' moment blank at a startup is a missed signal. Ask about the on-call rotation, current infrastructure challenges, or how DevOps collaborates with ML engineers.