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.