LLMOps & Production Interview Questions
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LLMOps & Production Interview Questions logoLLMOps & Production Interview Questions

50 questions that come up again and again in AI Platform, LLMOps and Forward Deployed Engineer interviews: versioning, CI/CD with eval gates, safe rollouts, GPU serving on Kubernetes, observability, incidents, cost, and running AI reliably at scale.

Last updated: 06 Oct, 2026

The questions get harder as you go. The easy questions cover the foundations of operating LLM applications: versioning, environments, deployment options, metrics, SLOs, tracing, testing and governance. The intermediate questions cover day-to-day production engineering: CI/CD, rollouts and rollbacks, GPUs on Kubernetes, autoscaling, RAG and batch operations, load testing, alerting, incidents and security. The hard questions cover platform and cluster designs, multi-region resilience, migrations, cost programmes, agent operations and real-world debugging.

How to use these questions

  • 30-second answer first. Every question opens with a short answer. Say it out loud before reading on. In a real interview, lead with this, then go deeper if the interviewer asks.
  • Then the depth. The detailed answer is what a senior interviewer listens for: trade-offs, failure modes, and how you'd actually build it.
  • Common mistakes. The answers that make interviewers lose interest. Knowing them helps as much as knowing the right answer.
  • Follow-ups to expect. Interviewers rarely stop at one question. Prepare these and the conversation stays on your ground.

Question types

TypeQuestionsWhat it tests
Concept34How things work
Scenario10"This broke in production, what do you do?"
System design6Whiteboard rounds

Tips for LLMOps interviews

  • Version everything that changes behaviour. Prompts, model versions, indexes, guardrails and agent graphs, not just code. Then log the versions per request.
  • Know your mitigation levers. Kill switches, flags, rollbacks, fallbacks, HITL mode. Interviewers ask what you'd do in the first ten minutes.
  • Scale on the right signals. Queue depth, KV-cache usage and TTFT, not CPU. Mention cold-start times for GPU pods.
  • Cost per outcome, not per call. Tie token and GPU spend to resolved tickets or completed tasks.
  • Behaviour can change without a deploy. Provider updates, data drift and stale indexes. Scheduled evals and drift alerts catch them.

Easy: foundations

#QuestionType
Q1What is LLMOps, and how does it differ from MLOps and DevOps?Concept
Q2Describe the lifecycle of an LLM application in production.Concept
Q3What should be versioned in an LLM application?Concept
Q4How do dev, staging and production environments work for LLM applications? What's tricky about them?Concept
Q5What are the main options for deploying LLMs, and how do you choose?Concept
Q6What are the key production metrics for an LLM service?Concept
Q7How do you define SLOs for an LLM-powered service?Concept
Q8What should you log and trace for LLM requests? What is OpenTelemetry's role?Concept
Q9How should API keys and secrets be managed in LLM applications?Concept
Q10What's different about containerising LLM applications and model servers?Concept
Q11What does a testing strategy look like for an LLM application?Concept
Q12Why are feature flags and config-driven behaviour important in LLM applications?Concept
Q13How do you track and attribute LLM costs in an organisation?Concept
Q14How do you handle model deprecations and version changes from providers?Concept
Q15How should prompts, outputs and traces be governed in terms of retention and access?Concept

Intermediate: building and debugging

#QuestionType
Q16Design a CI/CD pipeline for an LLM application with evaluation gates.System design
Q17Explain shadow, canary, blue-green and A/B deployments for LLM changes. When do you use each?Concept
Q18How do you design rollback for an LLM application? What exactly do you roll back?Concept
Q19How do you run GPU inference workloads on Kubernetes?Concept
Q20What metrics should you autoscale self-hosted LLM servers on, and why not CPU?Concept
Q21How do you manage model weights and artifacts in production?Concept
Q22How do you serve many fine-tuned variants (e.g. per-customer LoRA adapters) efficiently?Concept
Q23After a deploy, p95 latency doubled. How do you investigate?Scenario
Q24Your self-hosted vLLM servers start throwing out-of-memory errors or preempting requests under load. What do you do?Scenario
Q25Users see intermittent timeouts because the LLM provider's tail latency is high. How do you make the system robust?Scenario
Q26What does operating the RAG data pipeline involve in production?Concept
Q27How do you operate large batch inference jobs reliably and cheaply?Concept
Q28How do you load test an LLM service realistically?Concept
Q29How do you do capacity planning for a self-hosted LLM deployment?Concept
Q30What should you alert on for an LLM application, and how do you avoid alert fatigue?Concept
Q31What does an incident response process look like for LLM applications?Concept
Q32What security practices are specific to operating LLM systems (supply chain and runtime)?Concept
Q33How do you handle multi-tenancy and noisy neighbours in a shared LLM platform?Concept
Q34Your outputs changed in style and quality overnight with no deploy. You suspect the provider updated the model. How do you confirm it and respond?Scenario
Q35A product manager edited a prompt directly in the prompt-management UI, and it broke a production workflow. How do you fix the process without slowing everyone down?Scenario

Hard: production and design

#QuestionType
Q36Design a release management system for prompts, models, retrieval configs and agents across many teams.System design
Q37Design a self-hosted inference cluster on Kubernetes serving five open models (chat, code, embeddings, reranker, small classifier) with SLOs.System design
Q38Design a continuous improvement pipeline that periodically fine-tunes a model on production feedback.System design
Q39Design a multi-region, highly available LLM-powered service with failover.System design
Q40Your company wants to move a high-volume feature from a commercial LLM API to a self-hosted open model. Plan the migration.Scenario
Q41Leadership asks you to cut LLM inference costs by 50% this quarter without hurting quality. What's your plan?Scenario
Q42How do you plan disaster recovery and business continuity for AI features?Concept
Q43How do you operate long-running autonomous agents in production?System design
Q44How do you optimise GPU costs for self-hosted LLM inference?Concept
Q45Your self-hosted model servers degrade gradually: latency creeps up over days until restarts fix it. How do you debug it?Scenario
Q46What operational practices support compliance and auditability for AI systems?Concept
Q47Write the summary of a post-mortem for an LLM incident: an agent's retry loop caused a ₹4 lakh overnight cost spike.Scenario
Q48What's different about operating LLMs on edge devices or on-device?Concept
Q49How do you choose between building and buying each layer of the LLMOps stack?Concept
Q50You join a company as its first LLMOps engineer. There are 10 ad-hoc LLM applications built by different teams. What do you do in your first 90 days?Scenario
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