AI Engineer (Senior/Lead)

We are looking for a Senior/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