Senior Azure MLOps Engineer

We are seeking a Senior Azure MLOps Engineer to join our team and help deploy and operate ML and foundation models reliably in production on cloud infrastructure. You will collaborate with researchers, data scientists, and engineers to build robust pipelines, automation, and monitoring. Apply to help shape scalable AI delivery! Responsibilities Design production-grade MLOps pipelines for training, deployment, and monitoring Build CI/CD workflows for model packaging, validation, and rollout Deploy ML and foundation models on Microsoft Azure and Kubernetes platforms Implement model lifecycle management practices across versioning and promotion stages Develop monitoring and alerting for model performance, drift, and service health Automate testing and quality gates for ML code and pipelines Collaborate with data scientists and researchers to operationalize experiments Optimize ML deployment performance and reliability for production workloads Coordinate cross-functional delivery with platform and engineering teams Maintain infrastructure as code to enable reproducible environments Troubleshoot production issues across pipelines, services, and runtime dependencies Requirements 3+ years of experience deploying ML models to production environments Hands-on experience with Microsoft Azure for ML workloads Strong experience building MLOps pipelines with CI/CD and model lifecycle management Production project experience operating ML services with monitoring and incident response practices Strong Python skills with PyTorch and/or TensorFlow expertise Strong software engineering skills in testing, code quality, and automation Strong collaboration skills working with ML researchers and cross-functional teams Azure DevOps experience supporting CI/CD workflows Terraform experience managing infrastructure as code Upper-Intermediate English level (B2) Nice to have Azure Kubernetes Service (AKS) experience running ML workloads Databricks experience supporting ML pipelines or feature workflows Google Cloud Platform experience with ML services or infrastructure