Description

Job Description

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.""- 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).""- Experience with feature stores, model registries, and model monitoring stacks.- GPU optimization and distributed training experience.- Experience with responsible AI toolkits and compliance requirements."Python, TensorFlow, PyTorch, Docker, REST APIs

Key Skills

Education

Any Graduate