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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