MLOps interview: questions and cases
MLOps interview questions: ML CI/CD pipelines, feature stores, monitoring, rollback. Mid/senior prep for 2026.
MLOps interviews test production skills — not just training models. Recurring themes below.
Pipeline & deployment
Walk through training → staging → prod. Model and data versioning (DVC, MLflow). Rollback strategy. Canary or shadow deployments for models.
Infra & cost
Kubernetes vs serverless inference. GPU autoscaling. Batch vs online cost optimization. IaC (Terraform) to reproduce environments.
Common case study
“Production metrics degraded — what do you do in 24 hours?” Log collection, distribution checks, rollback, post-mortem, regression tests.
Keep exploring
AI jobs you may like
Home feed and discovery ranking models for Spotify listeners worldwide.
GenAI tooling for podcast creators — Boston tech hub.
Kernel and inference optimizations for LLM serving — Austin AI research lab.
Graph neural networks for drug target discovery — SF biotech partnerships.
Real-time voice agents and telephony LLM integrations — Denver engineering office.
Sources: US AI/tech market data (2026), Ganloss job listings, public training catalogs.