We are seeking a Senior Data Engineer to architect, develop, and operationalize enterprise-grade AI agents, multi-agent workflows, and AI/ML solutions leveraging Python, Azure AI Foundry, Semantic Kernel / Microsoft Agent Framework, LangGraph, LangChain, AutoGen, and Strands SDK. In this role, you will partner with architects and data teams to advance solutions from prototype to scalable, dependable production. Responsibilities Architect, develop, and maintain enterprise-grade AI agents and multi-agent workflows for business use cases Incorporate and operationalize AI/ML models and agent workflows within scalable production environments Build orchestration with Semantic Kernel / Microsoft Agent Framework, LangGraph, LangChain, AutoGen, and/or Strands SDK Apply RAG, grounding, and prompt optimization to enhance response quality and minimize hallucinations Connect agents with internal and external systems through APIs and connectors Create and deploy error handling, fallback flows, and escalation paths Conduct testing and assessment of agents and automation workflows Work with architects and data teams to convert requirements into deployable AI solutions Assist with deployment, CI/CD, and release management using Azure DevOps Track and steadily enhance agent performance, reliability, and cost efficiency Requirements Skilled in Python development for AI/agent solutions, paired with strong general scripting ability Practical experience creating agents and multi-agent workflows using one or more orchestration frameworks: Semantic Kernel / Microsoft Agent Framework, LangGraph, LangChain, AutoGen, Strands SDK Deep knowledge of Azure AI Foundry for developing, deploying, and running agents at scale (hosted agents, agent service, memory, evaluation) Strong grasp of LLM concepts: prompt engineering, RAG, grounding, tool/function calling, hallucination mitigation, and evaluation Proficiency in API integration (REST APIs, JSON, authentication through OAuth2 / API keys) Ability to design agent behavior: state and memory management, tool use, error handling, fallback, and escalation paths Capacity to work with architects and data engineering teams to deliver scalable, production-grade solutions English proficiency at B2 level or above Nice to have Experience with Azure AI Services and the wider Azure stack (Blob Storage, Container Services) Familiarity with multi-cloud agent deployment (e.g., AWS Bedrock via Strands SDK) Knowledge of Model Context Protocol (MCP) for tool/connector integration Understanding of LLMOps / MLOps: monitoring, evaluations, and cost/latency optimization for AI Agents Background with vector databases, retrieval infrastructure for RAG, and Docker Proficiency in Microsoft Graph API integration and delivering within enterprise or regulated/validated environments (e.g., GxP), including governance and observability tooling
