Senior ML Engineer

Senior ML Engineer

Production ML engineering patterns for model deployment, MLOps infrastructure, and LLM integration.

Core Capabilities

• Model deployment — export to ONNX/TorchScript/SavedModel, containerize, canary rollout, and serve via FastAPI, Triton, TF Serving, TorchServe, or Ray Serve with p95<100ms / error<0.1% gates.
• MLOps pipelines — feature stores (Feast/Tecton), experiment tracking (MLflow/W&B), model registry, A/B testing, and drift-triggered retraining.
• LLM integration — provider abstraction, retry/fallback with exponential backoff, token counting, response caching, cost tracking, and Pydantic output validation.
• RAG systems — vector database selection, chunking strategies, ingestion, retrieval, and reranking.
• Model monitoring — latency/error tracking, input drift detection (KS test, PSI), prediction-shift alerts, and automated retraining triggers.

When to Use

• Deploying a trained model to production with canary rollout and monitoring.
• Standing up MLOps infrastructure (feature store, registry, retraining).
• Integrating LLM APIs with provider abstraction and cost control.
• Building a RAG pipeline (vector DB + chunking + retrieval + reranking).
• Setting up drift detection and model-health alerting.

Clarify First

Before generating artifacts, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Task — model deployment / RAG pipeline build / monitoring setup (selects the script and workflow)
• Serving target & rollout — container vs K8s and canary vs direct (drives the generated Dockerfile/manifests and health gates)
• Model or data interface — the input/output contract, and for RAG the corpus + vector store (shapes the scaffold)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Tools

All tools support --verbose/-v and emit JSON (status, start_time, end_time, processed_items) to stdout. See references/tool-reference.md for full flag detail.

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

• references/production-ml-workflows.md — the five step-by-step workflows (model deployment, MLOps setup, LLM integration, RAG, monitoring) with all code templates, serving/vector-DB/chunking/cost tables, the troubleshooting matrix, and success criteria. Read when executing any workflow.
• references/tool-reference.md — full flag/parameter tables and output formats for the three scripts. Read when scripting the tools.
• references/mlops_production_patterns.md — model deployment pipeline with Kubernetes manifests, feature store architecture with Feast examples, model monitoring with drift detection code, A/B testing with traffic splitting, automated retraining with MLflow. Read when building MLOps infra.
• references/llm_integration_guide.md — provider abstraction layer, retry/fallback with tenacity, prompt templates (few-shot, CoT), token optimization with tiktoken, cost calculation and tracking. Read when integrating an LLM.
• references/rag_system_architecture.md — RAG pipeline implementation code, vector database comparison/integration, chunking strategies, embedding model selection, hybrid search and reranking. Read when building a RAG system.