Senior AI Full-Stack Engineer

GENNTE Technologies; Dallas (TX); On-site; USD 120,000 - 150,000; Full time

Job summary

GENNTE Technologies is seeking an experienced engineer to lead the refactor of our orchestration layer, migrating SK Native Plugins, function calling, memory, and prompts to the Agent Framework's agents/workflows model, on OpenShift.

You will own integration of Semantic Kernel with Microsoft Agent Framework, Python/Django, and Azure AI Services, ensuring API contracts, RBAC, and data retrieval quality remain intact while delivering parity with existing systems.

Qualifications

• Must have hands-on experience refactoring SK plugins, planners, function calling, and memory to Agent Framework agents/workflows.
• Experience integrating Semantic Kernel with Microsoft Agent Framework in production environments.
• Proficiency in Python and Django to rebuild orchestration layers without breaking APIs.

Responsibilities

• Refactor SK Native Plugins, Function Calling, Prompt Templates, Memory/Chat History onto the Agent Framework's model.
• Migrate and deploy services on OpenShift with proper RBAC and permissions.
• Ensure retrieval, reranking, and citation paths remain intact for Azure OpenAI / Azure AI Search.

Skills

Semantic Kernel

Microsoft Agent Framework

Python/Django

Azure AI Services / RAG

Tools

OpenShift / containerization

Observability & Evaluation

Model Context Protocol (MCP)

Job description

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.