AI Engineer
- Role
- AI / ML
- Employment
- Full-time
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No BS summary
AI engineer for production LLM/agent systems, strong in Python and FastAPI, with at least one shipped AI/LLM app. Needs working fluency with agent frameworks such as LangGraph, context engineering, eval loops, regression testing, and harness engineering. EdTech experience, React, Langfuse, and knowledge graphs are a plus.
Core skills
Required skills
Optional skills
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. 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 — what broke, how you found out, what you changed. Scale matters less than what you learned from its failures. Agent frameworks: working fluency with agent frameworks (we use LangGraph; equivalents fine) and a real point of view on context engineering. Testing discipline: the habit of testing what you build. AI-native coding with demonstrated harness engineering experience Desirable (not required): React and full-stack range; Langfuse or similar observability; knowledge graphs. Educational/Edtech domain experience Essential Traits for Success You build with AI, not just use it, proper harnesses, eval loops, and fast iteration. You can’t put an interesting problem down. You figure things out fast; we don’t need an exact-spec match if you have the base and the initiative. You favour non-standard evidence of skill, a portfolio of weird side projects beats a polished CV. Person Specification/Attributes Courage: Willingness to speak up, challenge the status quo, and embrace new challenges. Humility: Openness to learning, seeking help when needed, and a focus on serving others. Adventure: A passion for setting ambitious goals, tackling difficult tasks, and finding joy in the journey. Initiative: Proactive problem-solving, a sense of ownership, and a willingness to go above and beyond. Resilience: The ability to bounce back from setbacks, persevere through challenges, and emerge stronger. Employment Type This role is a full-time position. Preferred Time Zones The preferred time zones are GMT+2. Due to the considerable amount of virtual working and interaction with colleagues and customers in different physical locations internationally, it is essential that the successful applicant has the drive and ethic to succeed working in small teams physically but in larger efforts virtually. Self-drive to communicate constantly using web collaboration and video conferencing is essential. As an employee, you will be encouraged to continually develop your capability & attain certifications to reflect your growth as an individual.
What you'll do
- 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.
- 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.
- Work in collaboration with Anthropic Engineers, a team of AI engineers, product managers and data scientists to design world class learning experiences.
What they require
- 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 — what broke, how you found out, what you changed.
- Scale matters less than what you learned from its failures.
- Agent frameworks: working fluency with agent frameworks (we use LangGraph; equivalents fine) and a real point of view on context engineering.
- Testing discipline: the habit of testing what you build.
- AI-native coding with demonstrated harness engineering experience.
- Preferred: React and full-stack range.
- Preferred: Langfuse or similar observability.
- Preferred: knowledge graphs.
- Preferred: Educational/Edtech domain experience.
- You build with AI, not just use it, proper harnesses, eval loops, and fast iteration.
- You can’t put an interesting problem down.
- You figure things out fast; we don’t need an exact-spec match if you have the base and the initiative.
- You favour non-standard evidence of skill, a portfolio of weird side projects beats a polished CV.
- Courage: Willingness to speak up, challenge the status quo, and embrace new challenges.
- Humility: Openness to learning, seeking help when needed, and a focus on serving others.
- Adventure: A passion for setting ambitious goals, tackling difficult tasks, and finding joy in the journey.
- Initiative: Proactive problem-solving, a sense of ownership, and a willingness to go above and beyond.
- Resilience: The ability to bounce back from setbacks, persevere through challenges, and emerge stronger.
- The preferred time zones are GMT+2.
- Due to the considerable amount of virtual working and interaction with colleagues and customers in different physical locations internationally, it is essential that the successful applicant has the drive and ethic to succeed working in small teams physically but in larger efforts virtually.
- Self-drive to communicate constantly using web collaboration and video conferencing is essential.
Benefits
- As an employee, you will be encouraged to continually develop your capability & attain certifications to reflect your growth as an individual.
ALX Africa, a non-profit organisation under the ALX Foundation, is dedicated to unlocking the potential of Africa's digital future. It provides tech skills training and career acceleration programmes, with hubs in 8 cities across Africa, preparing learners for roles in software engineering, data science, and cybersecurity.