Remote
AI Engineer
About this role
About ALX Africa ALX Africa, a non-profit organisation under the ALX Foundation, is dedicated to unlocking the potential of Africa's digital future. Formerly part of Sand Tech Holdings, we've embarked on an independent journey to provide world-class tech skills training and career acceleration programmes. Our mission is to bridge the digital divide, upskill and re-skill talent, and create a generation of innovative leaders.
By 2030, we aim to empower 2 million Africans to secure sustainable tech careers. With hubs in 8 cities across Africa and counting, we provide safe access to quality learning and a dedicated network of expert instructors. Our innovative programmes equip learners with the practical skills and knowledge needed to succeed in today's rapidly evolving tech industry. Through a combination of rigorous coursework, industry partnerships, and hands-on projects, we prepare our students for in-demand roles in software engineering, data science, and cybersecurity.
We achieve this by: Providing young professionals with access to the most in-demand tech skills that will power the future. Empowering the next generation of technology innovators, entrepreneurs, and business leaders through challenging, real-world coursework. Building a lifelong, impactful community of tech professionals that support them at all stages of their career journey. Our impact thus far: 347k+ graduates since 2020 257k youth in work 31k youth starting own ventures 60k youth in jobs created by entrepreneurs Visit our website www.alxafrica.com to learn more about our digital revolution.
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.