AI Infrastructure Engineer

About the Role

The AI Office is seeking an AI Infrastructure Engineer to own the reliability of the Azure-based AI and analytics platform and to operationalize AI solutions into production. This role sits at the intersection of cloud infrastructure, DevOps, MLOps, and AIOps—keeping production data and model pipelines running while provisioning and deploying the environments new AI use cases depend on. It supports a portfolio spanning commodity forecasting, industrial optimization, credit risk, and turnaround and maintenance AI initiatives.

Effort Allocation

Platform operations & infrastructure support 50%

Deployment & containerization 25%

Model & pipeline delivery support 25%

Required Qualifications

• 5+ years of experience in DevOps, cloud infrastructure, or MLOps roles.

• Strong hands-on Azure experience: AKS, Azure Functions, App Service, Azure ML, Container

Registry, Key Vault, Storage, and Entra ID / App Registrations.

• Proficiency in Python and shell scripting.

• Ability to manage cloud services using Infrastructure-as-Code tools.

• Docker containerization and CI/CD pipeline experience (Azure DevOps preferred).

• Working knowledge of Kubernetes operations and troubleshooting.

• Networking fundamentals: private endpoints, firewall rules, DNS, and SSL/TLS.

• Demonstrated experience deploying and supporting machine learning models in production.

• ETL development and data pipeline monitoring.

• Strong written and verbal communication; able to work directly with enterprise IT, security, and

network teams to drive tickets to resolution.

Preferred Qualifications

• Azure certifications (AZ-104, AZ-400, or DP-100).

• Experience with Microsoft Fabric or Azure Synapse.

• Experience with Azure AI tools (Foundry, Search, etc.) is a plus.

• Infrastructure-as-code experience (Terraform, Bicep, or ARM templates).

• Experience in an enterprise environment with formal change control and security review

processes.

• Exposure to Streamlit, FastAPI, or similar Python application frameworks