MLOps and LLMOps Production Guide: Deploy, Observe, and Roll Back AI Systems

MLOps and LLMOps interviews test whether you can operate AI like software with extra uncertainty. Interviewers ask about deployment pipelines for models, prompts, indexes, and eval artifacts—each with different rollback semantics and blast radii.

Observability stacks must capture traces, token usage, cost per feature, retrieval metrics, and user outcomes. OpenTelemetry-style instrumentation across gateways, retrievers, and model providers enables cross-team incident response.

Drift appears as prompt drift, embedding drift, and user intent shift—not just traditional feature drift. Candidates describe weekly reviews of slice metrics, automatic alerts on faithfulness drops, and playbooks that distinguish data issues from model issues.

CI/CD for AI includes eval gates, synthetic traffic, and canary releases with automatic rollback. Interviewers probe how you version prompts alongside code and prevent configuration skew between regions.

Incident response for AI products requires replay tools, feature flags for model routes, and communication templates for customer-facing quality events. Production maturity means measurable MTTR improvements quarter over quarter.