senior-ml-engineer

# Senior ML Engineer

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

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## Table of Contents

- [Model Deployment Workflow](#model-deployment-workflow)
- [MLOps Pipeline Setup](#mlops-pipeline-setup)
- [LLM Integration Workflow](#llm-integration-workflow)
- [RAG System Implementation](#rag-system-implementation)
- [Model Monitoring](#model-monitoring)
- [Reference Documentation](#reference-documentation)
- [Tools](#tools)

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## Model Deployment Workflow

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 < 100ms, error rate < 0.1%

### Container Template

```dockerfile
FROM python:3.11-slim

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY model/ /app/model/
COPY src/ /app/src/

HEALTHCHECK CMD curl -f http://localhost:8080/health || exit 1

EXPOSE 8080
CMD ["uvicorn", "src.server:app", "--host", "0.0.0.0", "--port", "8080"]
```

### Serving Options

| Option | Latency | Throughput | Use Case |
|--------|---------|------------|----------|
| FastAPI + Uvicorn | Low | Medium | REST APIs, small models |
| Triton Inference Server | Very Low | Very High | GPU inference, batching |
| TensorFlow Serving | Low | High | TensorFlow models |
| TorchServe | Low | High | PyTorch models |
| Ray Serve | Medium | High | Complex pipelines, multi-model |

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## MLOps Pipeline Setup

Establish automated training and deployment:

1. Configure feature store (Feast, Tecton) for training data
2. Set up experiment tracking (MLflow, Weights & Biases)
3. Create training pipeline with hyperparameter logging
4. Register model in model reg...