Lead Machine Learning / MLOps Engineer

EPAM Systems • San Jose, CA

1d

About The Position

We are seeking an experienced and driven Machine Learning / MLOps Engineer to bridge machine learning research and production systems. The role develops, fine-tunes, deploys, and scales machine learning and foundation models in production, while building robust MLOps infrastructure and optimizing multi-GPU workloads. The engineer collaborates with cross-functional teams to deliver AI solutions.

Requirements

• Hands-on experience developing, fine-tuning, and deploying ML and foundation models in production environments.
• Strong Python programming skills with deep expertise in PyTorch and/or TensorFlow.
• Proven experience building and operating MLOps pipelines, including CI/CD, model tracking, registry, and monitoring.
• Experience deploying ML workloads on Azure and Kubernetes (AKS).
• Strong background in software engineering best practices, including unit and integration testing, version control, code quality, and automation.
• Experience managing multi-GPU workloads and optimizing inference and training performance.
• Ability to work effectively and collaboratively with ML researchers, data scientists, and cross-functional engineering teams.

Responsibilities

• Develop, fine-tune, and deploy machine learning and foundation models into robust production environments.
• Build, scale, and operate end-to-end MLOps pipelines, including CI/CD for ML, model lifecycle management, monitoring, and observability.
• Deploy and manage ML workloads on Azure and Kubernetes (AKS), ensuring high availability, scalability, and security.
• Manage multi-GPU workloads and optimize model performance, latency, and resource utilization.
• Uphold software engineering standards, including automated testing, high code quality, CI/CD pipelines, and infrastructure as code.
• Partner with ML researchers, data scientists, and engineering teams to translate research breakthroughs into production-ready systems.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed