AI Engineer

Role Summary

Project A is the AI layer of ALX's learning platform: onboarding and profiling, a project guide that works alongside learners, the Project-Deconstructor, and the content mappers that connect it all to a competency model. These began as prototypes; the AI Engineer's job is to make them reliable products. That means owning the systems learners actually touch, the context engineering that decides what an agent knows at any given moment, the tooling and service interfaces agents work through (such as the LLM's access to the learner's artifact window), and the day-to-day work of keeping long-running, multi-step agents reliable when real learners do unexpected things.

You will work in collaboration with Anthropic Engineers, a team of AI engineers, product managers and data scientists to design world class learning experiences.

Specific Responsibilities

Production AI Products

• Own AI products in production, stable, observable, with regressions caught by evals before learners find them.
• Take the next prototype from working demo to maintained product, with evals built in from the start rather than bolted on.

Agent Architecture & Context Engineering

• Design the context and tooling architecture for the agents, what is in context, when, and why.
• Build and maintain the service interfaces agents work through, such as the application's access to the learner's artifact window.
• Keep long-running, multi-step agents reliable under real-world learner behaviour, with clear failure modes and recovery.
• Build the habit of testing what you ship — eval loops, regression checks, and fast iteration as a default way of working.

Skill Requirements - Essential

• Python & FastAPI: strong, production-grade experience with real users.
• Shipped LLM applications: at least one AI/LLM application you have shipped and can discuss in detail.