We are looking for a Lead AI Agent Developer to design, build, and operationalize production-grade AI agents, multi-agent workflows, and AI/ML solutions using Python, Azure AI Foundry, Semantic Kernel / Microsoft Agent Framework, LangGraph, LangChain, AutoGen, and Strands SDK. You will work alongside architects and data teams to take solutions from prototype to scalable, reliable production. Responsibilities Design, build, and maintain production-grade AI agents and multi-agent workflows for enterprise use cases Integrate and operationalize AI/ML models and agent workflows into scalable production systems Implement orchestration using Semantic Kernel / Microsoft Agent Framework, LangGraph, LangChain, AutoGen, and/or Strands SDK Implement RAG, grounding, and prompt optimization to improve response quality and reduce hallucinations Integrate agents with internal and external systems via APIs and connectors Design and implement error handling, fallback flows, and escalation paths Perform testing and evaluation of agents and automation workflows Collaborate with architects and data teams to translate requirements into deployable AI solutions Support deployment, CI/CD, and release management (Azure DevOps) Monitor and continuously improve agent performance, reliability, and cost efficiency Requirements Proficiency in Python development for AI/agent solutions, with solid general scripting ability Hands-on experience building agents and multi-agent workflows with one or more orchestration frameworks: Semantic Kernel / Microsoft Agent Framework, LangGraph, LangChain, AutoGen, Strands SDK Expertise in Azure AI Foundry for building, deploying, and operating agents at scale (hosted agents, agent service, memory, evaluation) Solid understanding of LLM concepts: prompt engineering, RAG, grounding, tool/function calling, hallucination mitigation, and evaluation Skills in API integration (REST APIs, JSON, authentication via OAuth2 / API keys) Competency in designing agent behavior: state and memory management, tool use, error handling, fallback, and escalation paths Capability to collaborate with architects and data engineering teams to deliver scalable, production-grade solutions English proficiency at B2 level or higher Nice to have Background in Azure AI Services and the broader Azure stack (Blob Storage, Container Services) Experience with multi-cloud agent deployment (e.g., AWS Bedrock via Strands SDK) Familiarity with Model Context Protocol (MCP) for tool/connector integration Knowledge of LLMOps / MLOps: monitoring, evals, and cost/latency optimization for AI Agents Experience with vector databases, retrieval infrastructure for RAG and Docker Skills in Microsoft Graph API integration and delivering in enterprise or regulated/validated environments (e.g., GxP), including governance and observability tooling
