Lead embedded cycles end-to-end: discovery (workflow mapping + KPI definition), build/launch, and scale/standardize.
Ship full-stack internal systems that teams rely on daily; balance speed with maintainability.
Own and evolve shared internal AI software that multiple teams depend on.
Establish clear ownership paths for each deliverable: team-owned (shared), co-owned (team-specific), or handoff (when there’s a natural long-term home).
Drive measurable impact: define success metrics up front and iterate until adoption and KPI movement are real.
Create reusable templates, components, and playbooks so the next engagement ships faster and better.
Partner effectively across Engineering + stakeholders: communicate tradeoffs, scope, rollout plans, and change management clearly.

2+ years of professional software engineering experience shipping production systems (or equivalent).
Strong proficiency in backend scripting language such as Ruby or Python, and comfort working across backend + frontend (TypeScript/JavaScript/React).
Proven ability to deliver in ambiguity
You can work with subject matter experts to transform messy workflows into a scoped MVP and ship it quickly.
Hands-on experience building with LLMs and automating workflows; strong instincts for pragmatic solution design.
Strong communication skills and stakeholder leadership: you can run working sessions, demo clearly, drive adoption, and empathize with your internal customers.
AI fluency (required): You retool workflows with AI, instrument outcomes, and measure impact (time saved, error reduction, adoption, throughput).

Embedded AI Engineer

Key skills