GPU MLOps Engineer - United States

Overview In this role you will own the Azure-based AI/ML platform end-to-end, deploying models, provisioning GPU compute, securing environments, and optimizing costs. You’ll work with data scientists, ML engineers, and LLM engineers to keep the platform reliable and scalable. The position emphasizes building and automating ML workflows, cost awareness, and secure access across compute and data assets. This is a hands-on role at the intersection of ML deployment and cloud infrastructure that supports advanced AI initiatives. Compensation / Benefitsmedical, dental, visionflexible spending accounts and health savings accountslife insurancedisability benefitsretirement planspaid vacation/time off Responsibilities Build and maintain CI/CD pipelines (Azure Dev Ops) for training, validation, versioning, and deployment of ML models Provision and scale Azure GPU compute (ND/NC series) and container infrastructure (AKS/ACI) for training and simulation workloads Automate retraining/redeployment workflows in Azure ML pipelines as new data becomes available Implement access control, identity management, and secrets management (Microsoft Entra ID, Azure Key Vault) across compute, data, and model artifacts Monitor and optimize GPU/cloud spend (Azure Cost Management) and build cost visibility dashboards in Power BI Key requirements 10+ years in MLOps, Dev Ops, or Cloud Infrastructure Strong Azure experience — GPU compute (ND/NC), AKS, Docker Infrastructure-as-code experience (Terraform or Bicep)Cloud security fundamentals (IAM, network security, secrets management)Proven cost-optimization experience (right-sizing, autoscaling, spot/preemptible strategies)Hands-on experience with model registries and experiment tracking (Azure ML + MLflow)collaboration with cross-functional teamsproblem-solving mindsetstrong communication for security and cost discussions Azure GPU compute (ND/NC)Azure Kubernetes Service (AKS)Docker