AI Engineering Lead

Quick Facts

• Lead an AI Engineering team and build production AI tools and workflows
• Make technical decisions on whether to use AI vs deterministic software
• Own standards, evaluation, reliability, and cost per workflow

Description

You will lead and grow an AI Engineering team (with two platform engineers already in place) while continuing to build hands-on. The role focuses on practical judgment: many requests labeled “AI” should be implemented as deterministic code, automation, apps, or routed to other teams instead. You will ensure everything is built in a consistent, secure, measurable way.

Responsibilities

• Lead and grow the team; manage platform engineers, business analysts, and fullstack developers; help hire
• Own the technical call on each request and default to the lightest solution that works
• Route larger builds to domain teams (e.g., PLM, ERP, Photo Studio, workflow, Java) and ensure handoffs are picked up
• Set standards across architecture, design review, security/access, and CI/CD
• Build evaluation, regression testing, and monitoring for AI outputs
• Run and maintain shipped solutions: reliability, support, cost per workflow, and retirement of underperforming solutions
• Build hands-on: agents, integrations, and platform components
• Collaborate with business analysts on scoped problems and baselines for judging results

Requirements

• Production experience with LLM systems (agent/LLM solutions used by real users)
• Evaluation practice (tests/evaluation harnesses for non-deterministic output)
• Strong software engineering beyond AI (know when to use AI vs apps/integrations/rules)
• Platform depth: environments, secrets management, identity and RBAC, CI/CD
• Microsoft ecosystem fluency: Entra ID, Microsoft 365, SharePoint, and/or Microsoft Fabric or Azure data stack
• Integrator mindset: build on vendor models and APIs
• Leadership while building: led 2–6 engineers and still shipped code
• Cost awareness: reason about spend per workflow
• Strong written and spoken English

Benefits

You will shape a new function from the start with a working platform in place and a clear leadership mandate.

First-Year Milestones

• 3 months: existing AI tools are secured, monitored, and supported; every incoming request has a clear technical call
• 6 months: evaluation is in place for every AI solution in production; domain team handoffs work in practice
• 12 months: up to seven-person team ships on shared standards; each solution has known cost and measured results