Azure GPU MLOps Engineer | CI/CD & Cost Optimization

Molex; Austin (TX); On-site; USD 200,000 - 280,000; Full time

Benefits offered by this job

Medical

Dental

Vision

Retirement

Paid time off

Educational assistance

Infertility assistance

Paid parental leave

Adoption assistance

Job summary

The Molex GPU- MLOps Engineer will own the Azure platform layer end-to-end - deploying AI/ML models, provisioning and managing GPU compute, securing the environment, and continuously optimizing cost for our engineering AI/ML platform.

You'll join the platform team responsible for the compute, security, and cost backbone that powers our Azure AI/ML engineering tools, partnering closely with data scientists, ML engineers, and LLM engineers to keep everything running smoothly.

Qualifications

• 10+ years in MLOps, DevOps, or Cloud Infrastructure with real exposure to ML deployment and cloud management.
• Strong Azure experience — GPU compute (ND/NC), Azure Kubernetes Service, containerization (Docker).
• Infrastructure-as-code experience (Terraform or Bicep).
• Solid grasp of cloud security fundamentals (IAM, network security, secrets management).
• Demonstrated experience with cloud cost optimization (right-sizing, autoscaling, spot/preemptible strategies).
• Hands-on experience with model registries and experiment tracking (Azure ML + MLflow).

Responsibilities

• Build and maintain CI/CD pipelines (Azure DevOps) for training, validation, versioning, and deployment of ML models.
• Provision and scale Azure GPU compute (ND/NC) 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.

Job description

The Molex GPU- MLOps Engineer will own the Azure platform layer end-to-end - deploying AI/ML models, provisioning and managing GPU compute, securing the environment, and continuously optimizing cost for our engineering AI/ML platform.

You'll join the platform team responsible for the compute, security, and cost backbone that powers our Azure AI/ML engineering tools, partnering closely with data scientists, ML engineers, and LLM engineers to keep everything running smoothly.