AI Engineer (Senior/Lead)

We are looking for aSenior/Lead AI Engineer 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

• 5+ years of experience in strong 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)
• 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)
• Experience designing agent behavior: state and memory management, tool use, error handling, fallback and escalation paths
• Comfort collaborating with architects and data engineering teams to deliver scalable, production-grade solutions
• English proficiency at B2 level or higher

Nice to have

• Experience with Azure AI Services and the broader Azure stack (Blob Storage, Container Services)
• Multi-cloud agent deployment experience (e.g., AWS Bedrock via Strands SDK)
• Familiarity with Model Context Protocol (MCP) for tool/connector integration
• Expertise in LLMOps / MLOps: monitoring, evals and cost/latency optimization for AI Agents
• Experience with vector databases, retrieval infrastructure for RAG, Docker and Microsoft Graph API integration, as well as delivering in enterprise or regulated/validated environments (e.g., GxP), including governance and observability tooling