Overview The AI Platform Engineer - Microsoft AI Ecosystem is responsible for designing, implementing, and operating DeVry’s enterprise AI platform capabilities. This role combines platform engineering, AI architecture, solution development, and technical governance. The engineer will work across Azure AI Foundry, Azure OpenAI, Microsoft Fabric, Copilot Studio, Microsoft 365 Copilot, Azure AI Search, and related services to create reusable AI services that support business solutions across the university.Responsibilities Microsoft AI Platform Engineering Design and implement AI solutions using Azure AI Foundry, Azure OpenAI, Copilot Studio, and Microsoft FabricBuild reusable AI services, agent frameworks, prompt registries, evaluation pipelines, MCP integrations, and tool catalogsEstablish engineering standards for enterprise AI development AI Architecture & Reference Implementations Create reference architectures for agents, RAG (if required), tool calling, multi-agent workflows, and Human-In-The-Loop (HITL) workflowsProvide technical guidance on when to use Copilot, Foundry, Agentforce, ChatGPT Enterprise, Claude Enterprise, and private models Microsoft Fabric & Data Integration Design AI-enabled solutions leveraging OneLake, Lakehouse, Data Warehouse, Data Factory, and Power BIEstablish reusable AI-ready data patterns and provide guidance to data engineering as required Observability Provide guidance on monitoring, telemetry, evaluation frameworks, token tracking, cost allocation, prompt performance, and agent reliability metricsSupport deployment, rollback, versioning, and incident response Technical Governance Enablement Implement prompt versioning using GitHub, agent approval workflows, model routing controls, tool access controls, and usage monitoring. Automate quality control using agents in collaboration with innovation architect Cross-Functional Collaboration Being part of the Emerging Technology team, this role will collaborate with other team members as well as IT and business functionsTranslate business use cases into reusable technical solutions Qualifications Required 5+ years software engineering experience3+ years of hands-on experience building solutions using Azure AI services.Experience designing and implementing RAG architectures. Hands-on experience with Python, TypeScript/JavaScript, Git, CI/CD, Copilot Studio Azure AI Foundry, Azure AI Search, Semantic Kernel, LangChain, and MCP Preferred Azure AI Engineer Associate. Hands-on experience, Microsoft Fabric, Vector databases, and in regulated industries with more than 1000 employees