Azure GPU MLOps Engineer: Scale, Secure & Optimize

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

Benefits offered by this job

Medical

Dental

Vision

Flexible spending

Health savings account

Life insurance

Disability

Retirement

Paid vacation/time off

Educational assistance

Infertility assistance

Paid parental leave

Job summary

Molex is seeking a GPU- MLOps Engineer to own the Azure-based AI/ML platform, deploying models, provisioning GPU compute, and securing the environment in Austin, TX. You will partner with data scientists and ML engineers to keep the platform reliable and cost-efficient.

Join a team responsible for compute, security, and cost backbone, shaping pipelines, governance, and tooling to accelerate ML workflows while delivering scalable, compliant infrastructure.

Qualifications

• 10+ years in MLOps, DevOps, or Cloud Infrastructure.
• IaC experience with Terraform or Bicep.
• Strong cloud security fundamentals (IAM, network security, secrets).
• Experience with GPU-heavy ML workloads and cost optimization.

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 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.

Skills

MLOps

DevOps

Cloud Infrastructure

IAM & Security

Cost Optimization

Model Registries

Experiment Tracking

Azure DevOps

Tools

Terraform

Bicep

Azure DevOps

AKS

Azure ML

MLflow

Job description

Molex is seeking a GPU- MLOps Engineer to own the Azure-based AI/ML platform, deploying models, provisioning GPU compute, and securing the environment in Austin, TX. You will partner with data scientists and ML engineers to keep the platform reliable and cost-efficient.

Join a team responsible for compute, security, and cost backbone, shaping pipelines, governance, and tooling to accelerate ML workflows while delivering scalable, compliant infrastructure.