senior-ml-engineer

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---
name: "senior-ml-engineer"
description: ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization. Use when the user asks about deploying ML models to production, setting up MLOps infrastructure (MLflow, Kubeflow, Kubernetes, Docker), monitoring model performance or drift, building RAG pipelines, or integrating LLM APIs with retry logic and cost controls. Focused on production and operational concerns rather than model research or initial training.
triggers:
  - MLOps pipeline
  - model deployment
  - feature store
  - model monitoring
  - drift detection
  - RAG system
  - LLM integration
  - model serving
  - A/B testing ML
  - automated retraining
---

# Senior ML Engineer

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

---

## 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)

---

## 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 |

---

## 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 registry with version metadata
5. Configure staging deployment triggered by registry events
6. Set up A/B testing infrastructure for model comparison
7. Enable drift monitoring with alerting
8. **Validation:** New models automatically evaluated against baseline

### Feature Store Pattern

```python
from feast import Entity, Feature, FeatureView, FileSource

user = Entity(name="user_id", value_type=ValueType.INT64)

user_features = FeatureView(
    name="user_features",
    entities=["user_id"],
    ttl=timedelta(days=1),
    features=[
        Feature(name="purchase_count_30d", dtype=ValueType.INT64),
        Feature(name="avg_order_value", dtype=ValueType.FLOAT),
    ],
    online=True,
    source=FileSource(path="data/user_features.parquet"),
)
```

### Retraining Triggers

| Trigger | Detection | Action |
|---------|-----------|--------|
| Scheduled | Cron (weekly/monthly) | Full retrain |
| Performance drop | Accuracy < threshold | Immediate retrain |
| Data drift | PSI > 0.2 | Evaluate, then retrain |
| New data volume | X new samples | Incremental update |

---

## LLM Integration Workflow

Integrate LLM APIs into production applications:

1. Create provider abstraction layer f