AI Ops Engineer

Role Summary

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

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).

Experience with CI/CD and DevOps practices applied to ML (MLOps)