• Product Surfaces • Student-facing product interfaces — the core user experience that learners interact with daily • Real-time features that surface AI intelligence in ways that feel immediate and useful, not mechanical • Responsive, performant UI built on a shared component library • Backend & AI Integration • API routes and backend services connecting the user interface to the intelligence and data layers • AI orchestration integration: streaming responses, agent outputs, structured recommendations in context • Event-driven ingestion pipelines that translate platform activity into structured, reliable signals • Data & Infrastructure • Storage architecture and schema design for persistent user state — built to evolve as the product grows • Deployment infrastructure, CI/CD, and environment management — no dedicated DevOps to hand off to • Data access controls and audit logging that satisfy institutional security and compliance requirements • Quality & Velocity • End-to-end feature ownership from spec to production — including testing, monitoring, and iteration • Active use of AI-assisted development tooling to maintain high output without sacrificing code quality • Contribution to shared component libraries and codebase standards as the team grows • Stay on the bleeding edge of the AI ecosystem — Claude Code, Cursor, OpenAI Agent SDK, MCPs, and whatever comes next. • When a better way to build exists, find it and bring it in. • Please include a link to your portfolio/GitHub as part of your application.