ML Engineer

Role Summary: Builds, trains and tunes machine learning models. Translates data science experiments into scalable, production-ready ML solutions.

Responsibilities:

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, rollback strategies, and incident response.

Key Skills: Machine Learning, Python, ML Pipelines, Model Deployment, Model Serving, Data Engineering, Model Monitoring, Drift Detection, Performance Optimization, Automated Testing