senior-ml-engineer

Topics in the source

• Senior ML Engineer
• Table of Contents
• Model Deployment Workflow
• Container Template
• Serving Options
• MLOps Pipeline Setup
• Feature Store Pattern
• Retraining Triggers

Production ML engineering patterns for model deployment, MLOps infrastructure, and LLM integration. -- Model Deployment Workflow MLOps Pipeline Setup LLM Integration Workflow RAG System Implementation Model Monitoring Reference Documentation Tools -- Deploy a trained model to production with monitoring: 1. Export model to standardized format (ONNX, TorchScript, SavedModel) 2. Package model with dependencies in Docker container 3. Deploy to staging environment 4. Run integration tests against staging 5. Deploy canary (5% traffic) to production 6. Monitor latency and error rates for 1 hour 7. Promote to full production if metrics pass 8. Validation: p95 latency 0.2 | Evaluate, then retrain | | New data volume | X new samples | Incremental update | -- Integrate LLM APIs into production applications: 1. Create provider abstraction layer for vendor flexibility 2. Implement retry logic with exponential backoff 3. Configure fallback to secondary provider 4. Set up token counting and context truncation 5. Add response caching for repeated queries 6. Implement cost tracking per request 7. Add structured output validation with Pydantic 8. Validation: Response parses correctly, cost within bu

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