Remote - AI Engineering Manager (databricks)

State Farm State Farm · Insurance · Bloomington, IL +3

AI Engineering Manager to lead a team of 3-5 engineers building agentic workflows on Databricks. The role involves team leadership, developing an agentic harness integrated with Claude for SDLC tasks, creating production templates for Databricks pipelines, fostering a developer community, and ensuring production readiness. The focus is on shipping measurable impact, cutting cycle times, and reducing incidents.

What you'd actually do

  1. Lead a team of 3-5 embedded agentic engineers who work inside Product Oriented Delivery pods, helping engineers, analysts, and product owners ship twice as fast with twice the quality through agentic workflows.
  2. You'll build the agentic harness tuned to our existing infrastructure, write code alongside your team, run the developer community, and ensure a steady stream of innovation projects make it to production on Databricks
  3. Team Leadership (30%) – Hire, manage, and grow 3-5 embedded engineers. 1:1s, career development, removing blockers. You code 30-40% of the time.
  4. Agentic Harness (25%) – Build the agentic harness tuned to our infrastructure – hooks, connectors, and integrations with Claude for code quality checks, artifact generation, and next-best-action guidance through the SDLC.
  5. Databricks Solutions (20%) – Production templates for Medallion pipelines, Unity Catalog governance, MLflow, PySpark. Not PoCs – real systems with monitoring and runbooks.

Skills

Required

  • 2+ years managing engineering teams
  • Experience with embedded/distributed teams
  • Coaching mindset
  • Shipped end-to-end systems to production with real users, SLAs, and on-call
  • 1+ year operating production ML/AI systems
  • 2+ years production Databricks (Delta Lake, Unity Catalog, MLflow, PySpark)
  • Medallion architecture
  • Cost optimization
  • Anthropic Claude production experience (structured outputs, tool use, multi-turn)
  • Built developer tools with LLMs
  • Evaluation frameworks
  • Prompt engineering at scale
  • Production-grade Python
  • SQL expertise
  • CI/CD
  • API design
  • Code quality obsession
  • Threat modeling
  • Chaos engineering
  • Technical teaching
  • Influence without authority
  • Clear written communication
  • Stakeholder management

Nice to have

  • Developer platforms or CLI tools
  • DORA/SPACE metrics
  • Open-source contributions
  • Event streaming
  • Privacy regulations
  • Insurance/fintech/regulated industries
  • Lightweight UI skills (Streamlit, Gradio, FastAPI + HTMX)

What the JD emphasized

  • Production (Non-Negotiable)
  • Shipped end-to-end systems to production with real users, SLAs, and on-call.
  • 1+ year operating production ML/AI systems.
  • Production-grade Python.
  • Code quality obsession.

Other signals

  • shipping agentic workflows
  • production ML/AI systems
  • developer adoption
  • measured impact