Lead Microsoft Azure MLOps Engineer

We are looking for a seasoned Lead Microsoft Azure MLOps Engineer to join our team.

We are building and operating production-grade ML and foundation model platforms on Microsoft Azure, helping clients ship reliable AI at scale. You will lead MLOps engineering across CI/CD, model lifecycle management, and monitoring while partnering closely with researchers and engineers.

Responsibilities

• Lead design and delivery of end-to-end MLOps pipelines for training and deployment
• Build CI/CD workflows for ML services and model artifacts using Azure DevOps
• Deploy and operate ML workloads on Microsoft Azure and Kubernetes environments
• Implement model lifecycle management, monitoring, and alerting for production systems
• Harden software engineering practices through testing, automation, and code reviews
• Collaborate with ML researchers and data scientists to productionize foundation models
• Coordinate cross-functional engineering work to meet reliability and release goals
• Optimize infrastructure automation and reproducibility using Terraform
• Troubleshoot production issues across platform, pipeline, and runtime layers
• Guide technical decisions and mentor engineers to raise team standards

Requirements

• 5+ years of experience delivering production ML systems and model deployments
• 1+ years of technical leadership experience mentoring engineers or leading technical initiatives
• Strong hands-on experience with Microsoft Azure for ML workloads
• Hands-on experience with Kubernetes and Azure Kubernetes Service (AKS)
• Project delivery experience coordinating cross-functional teams and priorities
• Strong Python skills with PyTorch and/or TensorFlow expertise
• Strong software engineering practices in testing, code quality, and automation
• Hands-on MLOps experience with CI/CD, model lifecycle management, and monitoring
• Infrastructure as code experience with Terraform
• CI/CD platform experience with Azure DevOps
• Strong collaboration skills working with ML researchers and data scientists
• Excellent command of written and spoken English (B2+ level)

Nice to have

• Azure Kubernetes Service (AKS) expertise for GPU-based workloads
• Databricks experience supporting ML platform workflows
• Google Cloud Platform knowledge for multi-cloud ML deployments
• Distributed training framework experience for large-scale model training
• Performance optimization experience for GPU workloads and model inference

Benefits

• International projects with top brands
• Work with global teams of highly skilled, diverse peers
• Healthcare benefits
• Employee financial programs
• Paid time off and sick leave
• Upskilling, reskilling and certification courses
• Unlimited access to the LinkedIn Learning library and 22,000+ courses
• Global career opportunities
• Volunteer and community involvement opportunities
• EPAM Employee Groups
• Award-winning culture recognized by Glassdoor, Newsweek and LinkedIn