AI Engineer (llm)

State Farm State Farm · Insurance · Bloomington, IL +3 · Technology and UX

Software engineer to build and maintain AI-driven orchestration pipelines using LLMs, connectors, and enterprise data sources within the Power Platform ecosystem. Focus on developing agentic and autonomous workflows, implementing Model Context Protocol, engineering reusable AI components, building guardrails, optimizing LLM experiences, and developing observability frameworks. Requires experience with full-stack development (Javascript/React, Python), LLM tool-use frameworks (MCP), and AI/ML tools/APIs.

What you'd actually do

  1. Design, build, and maintain AI-driven orchestration pipelines that coordinate LLMs, connectors, and enterprise data sources across the Power Platform ecosystem.
  2. Develop agentic and autonomous workflows using emerging AI capabilities (Copilot Studio, agentic orchestration patterns, MCP, A2A protocol) to enable self-directed task execution and multi-stop reasoning.
  3. Implement Model Context Protocol (capital MCP) integrations to unify model access to enterprise systems, ensuring secure, governed, and scalable interactions between AI agents and organizational data.
  4. Engineer reusable AI components and abstractions (skills, actions, connectors) that accelerate adoption of AI across business areas and reduced duplication of effort.
  5. Collaborate with Microsoft product and engineering teams to evaluate and adopt new AI platform features, providing feedback that shapes roadmap alignment for enterprise scenarios.

Skills

Required

  • 3+ years of professional full stack development software engineering experience using Javascript/React and Python
  • Experience with the Model Context Protocol (MCP) or similar tool-use frameworks for LLMs
  • Experience working with or integrating AI/ML tools, APIs, or platforms (e.g., OpenAI API, Azure AI, AWS Bedrock, or similar)
  • Solid understanding of modern software development workflows including CI/CD, version control (Git), and code review practices
  • Hands-on experience building or customizing agentic AI workflows (e.g., multi-step autonomous coding agents, spec driven development, etc.)
  • Familiarity with IDE ecosystems and developer tooling (e.g., VS Code extensions, CLI tools)

Nice to have

  • Familiarity with Power Platform
  • Contributions to open-source projects related to AI, developer tooling, or code generation
  • Strong problem-solving skills and ability to work independently as well as collaboratively
  • Excellent written and verbal communication skills
  • Significant multi-tasking skills

What the JD emphasized

  • agentic orchestration
  • Model Context Protocol (MCP)
  • agentic AI workflows

Other signals

  • agentic orchestration
  • LLM integration
  • enterprise data sources
  • autonomous workflows
  • Model Context Protocol (MCP)
  • reusable AI components
  • guardrails
  • prompt tuning
  • grounding knowledge
  • retrieval pipelines
  • telemetry, monitoring, and observability