Senior AI/ML Engineer (Azure)

What project we have for you

The AI Product Family delivers enterprise LLM agents and AI services integrated with our Microsoft ecosystem. We run on Azure (Azure OpenAI/LLMs, Azure Functions, AI Search/vector, Service Bus, Storage, Key Vault) and connect securely to CRM, ERP, Microsoft Graph, and SharePoint. You’ll establish patterns for prompt engineering, multi-agent design, RAG, and ship reusable components that accelerate future solutions.

What you will do

• Lead AI architecture & patterns for LLM apps and multi-agent workflows (tool/function calling, memory, orchestration).

• Build hands-on services and agents in Python (primary) and/or .NET running on Azure Functions.

• Implement RAG pipelines (embeddings, chunking, grounding via Azure AI Search/vector); define evals and guardrails.

• Integrate securely with CRM, ERP, Microsoft Graph, SharePoint, and other enterprise APIs.

• Engineer and review prompts: establish style guides, versioning, test harnesses; improve quality, latency, and cost.

• Extend the platform with reusable agents, tools, connectors, and documentation.

• Mentor IT engineers; run code/prompt reviews and upskill sessions.

• Partner with DevOps on CI/CD, telemetry, cost tracking, security, and RBAC

What you need for this

Must-have

• 5+ years in software/AI engineering, including 2+ years building LLM applications.

• Deep experience with LLMs (Azure OpenAI/OpenAI/OSS), Semantic Kernel, prompt engineering, function/tool calling, and multi-agent design.

• Strong .NET; production services on Azure Functions.

• Production RAG experience (embeddings, vector stores – Azure AI Search), grounding, and evaluation.

• Proven API integration with Microsoft Graph, SharePoint, CRM, ERP (Business Central).

• Solid Azure fundamentals: Service Bus/eventing, Storage, Key Vault, Managed Identity/RBAC.

• Collaboration and mentoring skills; ability to lead design reviews and guide engineers.

• English B2

Will be a plus:

• .NET for services/integrations.

• Power Platform (Copilot Studio/Power Apps, custom connectors) to surface AI capabilities.

• Orchestration frameworks: Semantic Kernel, LangChain, LangGraph, AutoGen.

• LLMOps/observability: prompt/versioning, eval frameworks (Ragas/DeepEval), tracing (OpenTelemetry).

• Security, privacy/GDPR, Responsible AI practices.