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