Translate data science prototypes into production-grade ML services and pipelines. Build training and inference code with reproducibility, versioning, and automated testing. Implement scalable model serving (online/offline), batching, and latency/throughput optimization. Integrate model lifecycle tooling (tracking, registry, deployment automation, monitoring). Collaborate with Data Engineering on feature pipelines and data contracts. Own production health: drift detection, performance regression