senior-ml-engineer

Automate ML deployment by applying structured patterns for model serving, drift detection, and automated retraining. This skill coordinates MLOps workflows to productionize models reliably while monitoring feature stores and managing operational costs.

Monitor model drift in production ML systems by implementing automated detection workflows integrated with feature stores. This enables reliable, scalable AI operations through continuous observability and governance over your deployed models.

Yes, you can use Docker and Kubernetes for containerization and orchestration in scalable ML deployment. This skill applies these tools alongside experiment tracking platforms like MLflow or Weights & Biases to satisfy end-to-end lifecycle requirements.

Integrate RAG pipelines with existing ML models by applying real-time decisioning workflows that connect feature stores with LLM integration. This provides RAG-ready infrastructure for production ML systems requiring immediate inference capabilities.

Yes, this MLOps workflow supports cost-aware operations through resource optimization, observability, and compliance governance. It structures production ML operations to manage costs effectively while maintaining reliable drift detection and automated retraining cycles.