Senior Ml Engineer

This covers the production ML engineering stack you'd actually need: deployment workflows with Docker and serving options like Triton or Ray Serve, MLOps pipelines with feature stores and experiment tracking, and RAG system implementation with vector database comparisons. The monitoring section gives you drift detection code and alert thresholds that aren't just theoretical. What's useful here is the decision matrices, like when to use which chunking strategy or which LLM provider based on cost per token. It assumes you already know ML fundamentals and need the infrastructure patterns to ship models rather than just train them. The canary deployment workflow and retraining triggers are especially practical if you're moving from notebooks to production.