AI Product Engineer

Must-Have Skills: Strong software engineering fundamentals in at least one stack — clean APIs, sensible data models, tests, real debugging. Depth in one stack beats familiarity with five. Learning agility — a provable track record of entering an unfamiliar domain or codebase and shipping within weeks. This can substitute for most stack requirements. AI-native development with judgment — uses Claude Code or equivalent agentic tools as a primary way of building. Should be able to drive one live in the interview and explain where they took the keyboard back. High agency and builder's bias — takes an ambiguous ask and drives it to a shipped, measured fix. PostgreSQL competence — real queries, indexes, transactions, and the instinct that a wrong query is a customer-visible bug. Clear communication — can explain trade-offs to non-engineers without jargon, writes clearly, and is comfortable with real client contact. Working understanding of LLM agents — should be able to speak to what they've built with an LLM API and how it failed on them. Production-level depth is not required; we teach that. Good-to-Have Skills: Our environment (depth in some is a plus; candidate ramps on the rest): Python 3.11 (FastAPI, asyncio, pydantic) Anthropic and OpenAI SDKs Next.js / React / TypeScript at working proficiency Redis, Typesense AWS (EC2/ALB, RDS, SQS, SES) GitHub Actions, Datadog Genuine bonus: Production LLM/agent work (evals, guardrails, multi-agent, MCP) Prior forward-deployed or consulting-style client work Airflow or data pipelines Browser automation, OCR/vision Roles & Responsibilities: Build and operate AI agents and LLM-powered products — copilots, workflow agents, extraction pipelines, internal tools — and maintain/evolve existing ones. Own problems end to end: from the client conversation through design, code, deploy, and production support. Work AI-first, with humans owning judgment and outcomes — validate what the tools produce and own every line that ships. Take part in client and partner conversations: discovery, live demos of work in progress, pilot support. Write short proposals and design docs; decisions move through documents and PRs, not meeting chains. Run a lane end to end within a small, async-first team. Write tests where they protect what matters — agent behaviour and money paths. No coverage theatre. Apply AI security basics: prompt injection via untrusted content, least-privilege tool permissions, secrets hygiene, verifying anything inbound before acting on it.