legora Platform Engineer Interview: Questions & Prep (2026)

Legora is a legal AI company building tools that help law firms and corporate legal teams work faster and more accurately. Platform Engineers at Legora own the cloud infrastructure, developer tooling, CI/CD pipelines, and Kubernetes platforms that keep AI-driven legal products running reliably and securely.

Legora currently has 241 open roles, signalling active growth. As of July 2026, knok jobradar tracked 204 Platform Engineer openings across India. Bangalore leads with 29 postings, followed by Delhi (12), Pune (10), Hyderabad (5), Chennai (2), and Mumbai (1). A large share of the 204 openings did not specify a city, so opportunities exist well beyond those six metros.

Because Legora handles sensitive legal documents, the role carries higher-than-usual expectations around data security, compliance, and multi-tenant reliability. Candidates report that interviews typically combine infrastructure design questions, hands-on Kubernetes or CI/CD scenarios, and behavioural questions about collaborating with product and engineering teams.

These questions come up frequently in Platform Engineer interviews at companies similar to Legora. Candidates report they appear across technical and cross-functional conversations.

1. Kubernetes platform design. How have you designed or improved a Kubernetes-based platform to support multiple product teams sharing the same cluster?
2. CI/CD from scratch. Walk us through how you would build a CI/CD pipeline for a microservices application that includes security scanning and policy gates.
3. Infrastructure as code. What tools do you use for IaC, and how do you enforce consistency and prevent configuration drift across environments?
4. Security and compliance. Legora processes sensitive legal documents. How have you handled SOC 2, GDPR, or similar compliance requirements in a platform context?
5. Secrets management. How do you manage credentials and secrets in a cloud-native environment, and what mistakes have you seen teams make here?
6. Developer experience improvement. Describe a time you improved developer experience. What did you change, and how did you measure success?
7. Production incident handling. How do you respond to a production incident in a multi-tenant SaaS product where one tenant's issue must not impact others?
8. Cloud cost optimisation. What is your approach to reducing cloud spend without hurting reliability or developer velocity?
9. Observability for AI workloads. How would you design an observability stack for an AI inference service with variable latency and unpredictable load?
10. Cross-team conflict. Tell us about a time a platform decision created friction with another team. How did you resolve it?
11. Zero-downtime migrations. How do you approach migrating workloads to a new Kubernetes version or cloud region with minimal disruption?
12. Platform abstractions. What does a good internal developer portal look like to you, and where does abstraction become a mistake?

Use the STAR format throughout: Situation, Task, Action, Result. When speaking, keep each answer under three minutes and put the most detail on Action and Result.

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Q: Describe a time you improved developer experience. What did you change, and how did you measure success?

*Situation:* Our engineering teams were spending a large portion of each sprint waiting for slow CI pipelines and manually provisioning test environments. Developers were frustrated and feature velocity had dropped noticeably.

*Task:* I was asked to audit the pipeline and propose improvements that would cut wait times without introducing security gaps.

*Action:* I profiled each CI stage and found that Docker image builds were re-running unchanged layers on every commit. I introduced layer caching with a private registry. I also built ephemeral Kubernetes namespaces using Terraform and a small internal CLI so developers could spin up isolated test environments on demand, rather than booking time on a shared staging server.

*Result:* Pipeline wait times dropped noticeably. Developer satisfaction improved in our next quarterly survey, and we saw fewer environment-related bugs reach production because teams were testing more often.

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Q: How have you handled a production incident in a multi-tenant platform?

*Situation:* A memory leak in a shared service caused one tenant's workload to consume resources that degraded response times for other tenants on the same node pool.

*Task:* I needed to isolate the affected tenant immediately and find a permanent fix that prevented similar blast-radius issues in future.

*Action:* I used Kubernetes resource quotas and node affinity rules to cordon the affected namespace to its own node group. I then applied LimitRange objects across all namespaces so no single tenant could consume unbounded memory. I also added a memory-usage alert that fires well before the critical threshold is reached.

*Result:* The incident was contained quickly. The architectural change shipped the following week, and we have not had a cross-tenant resource contention issue since.

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Q: Tell us about a time a platform decision created friction with an engineering team. How did you resolve it?

*Situation:* I introduced mandatory image scanning in CI, blocking any image with high-severity CVEs from being deployed. Two product teams pushed back because it was blocking their release deadlines.

*Task:* My goal was to keep the security control in place while reducing the pain it caused for teams under tight timelines.

*Action:* I ran a working session with both teams to review which CVEs were actually exploitable in our environment versus theoretical risks. I built a time-boxed exception workflow where teams could request an override with a documented risk-acceptance sign-off from their engineering lead. I also set up weekly automated dependency-update PRs so teams stopped being surprised by new CVEs at release time.

*Result:* Both teams accepted the exception workflow as fair. The automated updates steadily brought the backlog down, and within a couple of months the number of blocked images had fallen close to zero.

STAR (Situation, Task, Action, Result) is the standard for behavioural questions. Keep Situation and Task brief. Put the weight on Action (what you specifically did) and Result (a concrete, observable outcome).

Situation-Complication-Resolution works well for technical design questions. Describe the starting state, explain what constraint or problem made it hard, then walk through your solution. This keeps answers focused rather than turning into a survey of every technology you know.

Before-After-Impact is a stripped-down version useful when an interviewer asks 'What did you improve?' State what existed before, what you changed, and what measurably improved. Skip backstory that does not explain your reasoning.

One rule that applies to all three: anchor your answers in decisions, not just actions. Interviewers at platform-focused companies want to hear why you chose a particular tool or approach, not just that you used it. A sentence like 'I chose Argo CD over Flux because our team was already familiar with Kubernetes CRDs and we needed progressive delivery features' tells the interviewer far more than 'I set up GitOps.'

Security-first thinking. Legora handles privileged legal data, so interviewers pay close attention to whether security is baked into your instincts or bolted on as an afterthought. Bring up access controls, secrets management, and compliance posture without being prompted.

Ownership and a reliability mindset. Platform engineers own the systems other engineers depend on. Interviewers look for candidates who treat uptime and developer velocity as personal responsibilities, not someone else's problem.

Collaboration with product and engineering teams. Platform work only succeeds if developers actually adopt the tools you build. Show that you know how to gather requirements, handle pushback gracefully, and iterate based on real usage.

AI and ML infrastructure awareness. Legora runs AI workloads on its platform. Even if you have not run LLM inference in production, familiarity with GPU scheduling, model serving patterns, or observability for non-deterministic outputs will stand out.

Pragmatism over perfection. Interviewers typically want to see that you can ship a good-enough solution quickly and iterate, rather than spend months designing the theoretically perfect architecture.

Week 1: Core platform skills. Revisit Kubernetes internals: namespaces, resource quotas, network policies, and admission controllers. Practice writing Terraform modules from scratch. Make sure you can explain the difference between blue-green and canary deployments and when you would choose each.

Week 2: Security and compliance. Read about SOC 2 Trust Service Criteria and how they translate to infrastructure controls (logging, access review, encryption at rest and in transit). Practice answering 'How would you design secrets management for a SaaS product?' out loud before your interview.

Week 3: Observability and incident response. Set up a local observability stack using open-source tools such as Prometheus and Grafana if you have not done so recently. Write out a brief incident response runbook for a hypothetical memory leak scenario so the thinking is fresh.

Week 4: Legora-specific prep. Read publicly available information about Legora's product and why data privacy matters in legal AI. Prepare three to four STAR stories covering: improving developer experience, handling a production incident, resolving cross-team conflict, and a decision you made that turned out to be wrong.

If you are applying to several companies at the same time, knok checks 150+ job sites nightly, applies to roles that match your resume, and messages HR on your behalf, so you can spend your prep time on interviews rather than on job hunting.

Ignoring security in technical answers. A candidate who designs a CI/CD pipeline without mentioning image scanning, secret injection, or least-privilege IAM roles signals a blind spot that matters a great deal to a company handling legal data.

Being too vendor-specific. Saying 'I would use AWS CodePipeline' when the interviewer is exploring your reasoning about pipeline design tells them very little. Lead with the principle (fast feedback, separation of build and deploy, policy gates) and then name the tool.

Not anchoring impact in evidence. 'I improved the pipeline' is far weaker than describing what changed and how you knew it worked. If you do not have exact numbers, describe what you measured and which direction it moved.

Treating platform work as purely technical. Legora is a growing company where platform decisions affect many teams. Candidates who never mention stakeholder communication, adoption challenges, or team feedback tend to score lower on cross-functional collaboration.

Skipping the 'why'. Interviewers ask 'why did you choose X over Y?' more often than candidates expect. For every tool or pattern you mention, have a one-sentence rationale ready.