Lead AI Agent Developer

Job description

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