onsite
MLOps Technical Lead - HCLTech
Engineering Manager
Lead end‑to‑end MLOps initiatives, designing scalable ML pipelines on AWS, Snowflake, and Kubernetes, while automating workflows with GitHub Actions and Jenkins. Drive model deployment, monitoring, and data integration using Python, Docker, and SQL.
About the role
Key Responsibilities
- Architect and maintain production‑grade ML pipelines on AWS (SageMaker, Lambda, ECS/EKS) and Snowflake.
- Design and implement containerized model deployments using Docker and Kubernetes for high‑availability and scalability.
- Automate CI/CD workflows with GitHub Actions or Jenkins, ensuring rapid, reliable model releases.
- Collaborate with data engineers to ingest, transform, and validate data using SQL and other ETL tools.
- Monitor model performance and drift, implementing alerting and retraining strategies.
- Mentor cross‑functional teams on MLOps best practices and emerging technologies.
Requirements
- Proven experience leading MLOps projects in a cloud environment.
- Strong proficiency in Python and familiarity with PyTorch or TensorFlow.
- Hands‑on expertise with Docker, Kubernetes, and AWS services.
- Experience with CI/CD tools such as GitHub Actions or Jenkins.
- Solid SQL skills and ability to work with large datasets.
Skills
mlopsawspythondockerkubernetesgithub actionssql