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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