AI Product Engineer, Evidence

Responsibilities

• Build AI-powered product features end-to-end — React/TypeScript UI through Node.js/Python backend — with real-time streaming, graceful degradation, and human-in-the-loop interaction patterns
• Translate AI capabilities — RAG pipelines, agentic workflows, structured reasoning — into interactions that feel natural to compliance practitioners
• Partner with AI Engineers to define API contracts and output schemas
• Advocate for the user's perspective in technical decisions; surface where model outputs break down in practice and iterate the product layer accordingly
• Build user-facing feedback loops that capture signal on AI output quality
• Instrument AI features with product-level observability — latency, engagement, task completion, drop-off — alongside integration and end-to-end tests
• Establish reusable patterns (streaming hooks, feedback components, AI state management) that accelerate future AI feature development
• Mentor engineers newer to AI product development; participate in design reviews

Requirements

• 3+ years of software engineering experience with a proven track record of shipping full-stack features in production
• 1+ years working on AI-powered product features
• Strong proficiency in React and TypeScript; experience building responsive, interactive UIs that handle async, streaming, and real-time data
• Solid Python and Node.js and TypeScript skills; experience designing and consuming APIs that integrate with LLM providers, orchestration frameworks, or AI services
• Hands-on experience building with LLM APIs (OpenAI, Anthropic, etc.); practical understanding of prompt engineering, context management, RAG, Quality/Evals, and structured outputs
• Familiarity with agent frameworks (LangGraph, LangChain, custom runtimes) and how to build product surfaces on top of multi-step agentic workflows
• Experience implementing streaming response patterns (SSE, WebSockets, chunked HTTP) and building UIs that handle progressive, partial, and uncertain outputs
• Strong intuition for user experience; track record of working closely with AI/ML engineers, product managers, and designers

Nice-to-Haves

• Experience in compliance, security, GRC, or other document-heavy enterprise domains
• Experience with MCP (Model Context Protocol) or similar tool-use interfaces for AI agents
• Exposure to RAG system design as a consumer who has debugged retrieval quality issues from the product layer
• Experience building feedback and annotation tooling that captures user signal on AI output quality