AI Platform Engineer

AI Platform Engineer

Seeking an AI Platform Engineer to join an AI & Digital Innovation team. This is a DevOps engineering role focused on the platform that powers Generative AI solutions: the Azure infrastructure, pipelines, and integration services that application teams use to build, host, and run AI agents and AI-enabled applications. The ideal candidate is a mid-level engineer with hands-on Azure experience who is comfortable with Infrastructure as Code (IaC) and brings enough development experience to write automation, build integration services, and partner closely with AI engineers. You will help make AI capabilities secure, repeatable, and easy to consume across the enterprise.

DUTIES & RESPONSIBILITIES

AI Platform Engineering:

• Build, configure, and maintain Azure AI platform resources, including Azure Foundry, Azure AI Search, and related services, using a federated model where application teams host workloads in their own subscriptions and resource groups.
• Deliver and operate Azure API Management (APIM) as the governed front door for AI models, APIs, and Model Context Protocol (MCP) servers, including policies for authentication, throttling, logging, and usage tracking.
• Support hosting patterns for AI agents and services on Azure Container Apps, Azure Foundry Projects, and Azure Functions on Container Apps.

Infrastructure as Code and Automation:

• Develop and maintain reusable IaC modules using Terraform for consistent provisioning of AI platform components.
• Automate manual and repetitive platform tasks, including onboarding, environment setup, access provisioning, and model deployments.

CI/CD and Development:

• Design and manage CI/CD pipelines using GitHub Actions and/or Azure DevOps for infrastructure, AI services, and agent deployments.
• Write and maintain code in C# (preferred) or Python for automation, integration services, MCP servers, and platform tooling.
• Partner with AI engineers and application teams to move prototypes into supportable, production-ready services.

Security, Identity, and Governance:

• Implement Azure authentication and authorization patterns such as managed identity, Microsoft Entra ID app registrations, role-based access control (RBAC), and Azure Key Vault.
• Apply network security controls including network scoping and virtual network integration.
• Support AI governance by enforcing approved model lists, data-handling guardrails, and auditable access across the platform.

Monitoring and Support:

• Implement observability for AI workloads using Azure Monitor, Application Insights, Log Analytics, and OpenTelemetry, including token usage and cost visibility.
• Troubleshoot build, deployment, and runtime issues across infrastructure, pipelines, and AI services, and collaborate with Infrastructure Cloud Engineering (ICE) and operations teams on handoffs and Day 2 support.
• Contribute documentation, reference patterns, and runbooks that help other teams adopt the platform.

MINIMUM REQUIREMENTS

• 3–5 years of experience in DevOps, cloud, or platform engineering roles.
• Hands-on experience with Microsoft Azure, including compute, networking, storage, and identity services.
• Working knowledge of Azure authentication patterns such as managed identity, Entra ID, and RBAC.
• Experience building CI/CD pipelines with YAML in Azure DevOps and/or GitHub Actions.
• Development experience in C# (preferred) or Python, plus scripting experience with PowerShell or Bash.
• Experience with Git-based source control and branching workflows.
• Strong troubleshooting and problem-solving skills, with a bias for automation.
• Clear communication skills and the ability to collaborate with development, infrastructure, security, and offshore teams.

PREFERRED EXPERIENCE

• Experience with Terraform Infrastructure as Code (IaC).
• Software development experience building APIs or services in C#/.NET (preferred) or Python.
• Exposure to Azure AI services such as Azure Foundry and Azure Foundry Projects.
• Familiarity with Generative AI concepts, including large language models (LLMs), retrieval-augmented generation (RAG), AI agents, and MCP.
• Experience with Azure API Management (APIM).
• Experience with containers and container platforms such as Docker, Azure Container Apps, or AKS.
• Experience in a regulated industry such as financial services, insurance, or title and escrow.