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