ML Engineer
Virtusa · New York, NY · 1 mo ago
EngineeringFull-time
About the role
Translate data science prototypes into production-grade ML services and pipelines.
Responsibilities
- 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
Requirements
- 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)
Qualifications
- Experience with feature stores, model registries, and model monitoring stacks
- GPU optimization and distributed training experience
- Experience with responsible AI toolkits and compliance requirements
Skills
- Python
- TensorFlow
- PyTorch
- Docker
- REST APIs