Virtusa Verified
Information Technology, IT Services, Consulting
ML Engineer
New York, New York, United StatesOnsiteFull TimePosted 2 months agoVisa sponsorship available
Role summary
We are seeking an experienced ML Engineer to bridge the gap between data science prototypes and production-ready ML services. You will be responsible for building robust training and inference pipelines, implementing scalable model serving solutions, and integrating essential ML lifecycle tooling. This role requires a strong foundation in software engineering, Python, ML frameworks (TensorFlow/PyTorch), containerization (Docker/Kubernetes), and API development. You will also own production health, including drift detection, performance monitoring, and incident response, leveraging MLOps best practices. Experience with feature stores, model registries, and responsible AI is highly valued.
- 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
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