Senior AI Full-Stack Engineer

Knowledge Search is a containerized, enterprise-grade RAG platform on Azure AI Services with a Django/Python, deployed on OpenShift.

Semantic Kernel is the current orchestration layer, and the SK → MS Agent Framework migration is a Q3 '26 roadmap item that unblocks Agentic Workflows and the Control Panel.

The refactor re-platforms SK Native Plugins, Function Calling, Prompt Templates, Memory/Chat History, and Planners/Agents onto the Agent Framework's agents/workflows model.

Must-Have Skills:

• Semantic Kernel + Microsoft Agent Framework — hands-on experience refactoring SK plugins, planners, function calling, and memory to Agent Framework agents/workflows (this is the critical, hardest-to-source skill)
• Python / Django — to rebuild the orchestration layer without breaking existing API contracts, RBAC, and permissions
• Azure AI Services / RAG — Azure AI Search (hybrid retrieval, semantic ranking), Azure OpenAI (inference + embeddings), preserving retrieval, reranking, prompt assembly, and citation paths

High-Priority Skills

• Model Context Protocol (MCP ) — for tool/connector integration and the Intelligence Layer channel
• OpenShift / containerization — to deploy migrated microservices on OCP
• Observability & Evaluation — Arize Phoenix + OpenTelemetry instrumentation, agent/agentic-system registration, and RAG quality/eval validation to confirm parity

Medium-Priority Skills

• Redis Enterprise (caching/session state) and Azure APIM (gateway, rate limiting)

The single most differentiated requirement is combined hands-on Semantic Kernel and Microsoft Agent Framework experience paired with Python/Django, since that is the exact intersection where the refactor happens. The remaining skills (Azure AI Search, RAG, OCP, observability) align with what the team already runs today.