AI Technical Lead

**Technical Lead/Architect** **Remote \- United States** **About the Role** We're a small team within Blackboard building a new AI\-native product. This is not a feature team, and it's not an enhancement to an existing product — we're building something new. We use Claude Code, Cursor, and AI\-assisted tooling to generate the majority of our code. Our engineers are architects, reviewers, and product thinkers — people who direct AI coding agents effectively rather than writing every line by hand. As Technical Lead, you set this culture. You will establish the norms, the workflows, and the standards for how AI\-native development actually works at scale on a small team. The person we're looking for is excited by this model, not threatened by it. You are the technical leader. You will design, architect, and build the core system — a stateful, multi\-layer architecture spanning data ingestion, decision logic, and AI\-powered delivery — and recruit a small senior team to build alongside you. This is a builder role first. If you're looking to step into a seat where you primarily direct others, this isn't the right fit. We need someone who still loves building — and is exceptional at it. You will own the full technical vision and be accountable for it. What You'll Build Core Intelligence Architecture * A multi\-layer system where each layer is decoupled, auditable, and independently evolvable * A persistent learner state model built to reason across complex, longitudinal user data * A rules\-based decision layer where logic is explicit and transparent — every recommendation can be explained Data \& Integration Layer * Event\-driven pipelines that translate real\-time platform activity into structured, reliable signals * APIs that connect the intelligence layer to the user\-facing product * LMS integration for real\-time data ingestion, designed to scale across platforms over time AI \& Agent Architecture * A multi\-agent system backed by shared state — precision\-bounded interventions, not open\-ended AI responses * Prompt engineering and guardrail systems that keep agent behavior auditable and aligned * Observability infrastructure so every system decision can be monitored, debugged, and improved Engineering Culture \& Team * Establish an AI\-native development culture: AI tools as the default, human judgment as the filter * Weekly ship cycles, high observability, and a bias toward iteration over speculation * Hire and mentor a small senior team (2–3 Product Engineers) as the product scales from v1 to v2 * Stay on the bleeding edge of the AI ecosystem — Claude Code, Cursor, OpenAI Agent SDK, MCPs, and whatever comes next. Make the call on what we adopt, when, and why. That judgment sets the standard for the whole team. **The Right Person** You believe that intelligence should be deterministic where it can be, and generative where it should be. You're skeptical of LLMs as decision\-makers but ex