Description

Key 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. Required Qualifications - 5+ years software engineering with 2+ years shipping ML models to production. - Strong Python skills and experience with ML frameworks (TensorFlow/PyTorch). - Experience with containers and orchestration (Docker/Kubernetes) and API development. - Understanding of ML system design (data leakage, training-serving skew, drift). - CI/CD and DevOps practices applied to ML workloads (MLOps). Preferred / Nice to Have - Experience with feature stores, model registries, and model monitoring stacks. - GPU optimization and distributed training experience. - Experience with responsible AI toolkits and compliance requirements

Key Skills

Education

Bachelor's degree