Senior Ml Engineer

Senior Ml Engineer

ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization. Use when the user asks about deploying ML models to production, setting up MLOps infrastructure (MLflow, Kubeflow, Kubernetes, Docker), monitoring model performance or drift, building RAG pipelines, or integrating LLM APIs with

Optimized workflow

This edition turns the source methodology into a repeatable agent workflow with explicit inputs, checkpoints and deliverables.

Quality standard

• Confirm scope and missing inputs before execution
• Ground decisions in available evidence and preserve source constraints
• Return an actionable result with assumptions, risks and next steps

Agent compatibility

The same core method is packaged for Claude, Codex, GPT, Gemini, Cursor and OpenCode.