AI Product Engineer

Key Responsibilities: - Own a focused portfolio, not a sprawling one. You'll carry a capped, ranked set of active initiatives (target: 2-3 max concurrently) with clear launch dates — depth over breadth. - Build, don't just spec. Prototype AI features directly — LLM-powered workflows, agents, automations — using tools like Python, LLM APIs, RAG pipelines, and no-code/low-code platforms — so initiatives can move without sitting in the dev backlog. - Lock scope before build starts. Get sign-off on a PRD before development begins; any new requirement after that goes through a lightweight change-request, not a silent scope add. - Run a real decision cadence. Every initiative has a named decision-owner and a standing checkpoint — no initiative sits in limbo waiting on "someone to decide." - Partner with Engineering as a peer, not a queue. Work directly with engineering leads on feasibility and sequencing up front, instead of discovering capacity constraints after commitments are made. - Own outcomes end-to-end — from opportunity sizing through UAT to post-launch metrics — and report actual turnaround time against target, not just status. Requirements & Qualifications: - A critical thinking problem solver who has the technical tools to build, validate and ship. - 3–5 years of product experience, including hands-on work shipping AI-powered features. - Practical experience with LLM APIs (OpenAI, Anthropic, Gemini or similar), prompt engineering, and retrieval-augmented generation (RAG). - Comfortable scripting in Python and querying in SQL well enough to build and test a working prototype yourself. - A track record of shipping automation or AI workflows using tools like n8n, Retool, Zapier. - Strong, direct communicator who can hold a scope line and drive a decision to closure.