Description

You will translate data science prototypes into production-grade ML services and pipelines, owning the full lifecycle from training to inference and monitoring.

This role is on-site.

Responsibilities

• Build training and inference code with reproducibility, versioning, and automated testing.
• Implement scalable model serving (online/offline) with batching and latency/throughput optimization.
• Integrate model lifecycle tooling including tracking, registry, deployment automation, and monitoring.
• Collaborate with Data Engineering on feature pipelines and data contracts.
• Own production health drift detection, performance regression, rollback strategies, and incident response.

Required Skills

• 5+ years software engineering experience.
• 2+ years shipping ML models to production.
• Strong Python skills and experience with ML frameworks (TensorFlow/PyTorch).
• Experience with containers and orchestration (Docker/Kubernetes).
• Experience with API development (REST APIs).
• Understanding of ML system design (data leakage, training-serving skew, drift).
• CI/CD and DevOps practices applied to ML workloads (MLOps).

Preferred Skills

• 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

Any Graduate