About The Role

• As our Financial Controller, you will play a critical role in shaping the financial future of ALX by ensuring the integrity, accuracy, and effectiveness of our global financial operations. You will lead the organization’s accounting, financial control, reporting, treasury, tax, and compliance functions while providing strategic financial insights that support sustainable growth and operational excellence.
• Working closely with the VP of Finance, executive leadership team, and external stakeholders, you will drive financial governance, strengthen internal controls, and ensure compliance across multiple jurisdictions. You will be responsible for building scalable financial processes, systems, and reporting frameworks that enable informed decision-making and support ALX’s continued expansion.
• The ideal candidate combines deep technical accounting expertise with strong commercial acumen, leadership capability, and a continuous improvement mindset. This is a highly visible role that will influence strategic decisions, optimize financial performance, and help build a world-class finance function.

Specific Responsibilities

Financial Reporting:

• Oversee the preparation of accurate and timely financial statements, including monthly, quarterly, and annual reports.
• Ensure compliance with local and international accounting standards (e.g., IFRS, GAAP).
• Coordinate with external auditors to ensure smooth audits and timely completion.

Financial Control & Risk Management:

• Implement robust financial controls and risk management processes to safeguard company assets.
• Monitor financial performance and identify potential risks and opportunities.
• Develop and implement strategies to mitigate financial risks.

Tax Compliance:

• Ensure compliance with all relevant tax laws and regulations.
• Manage tax filings, audits, and planning.
• Optimise tax strategies to minimise tax liabilities.

Treasury Management:

• Manage cash flow and optimise working capital.
• Oversee banking relationships and treasury operations.
• Implement effective treasury strategies to mitigate financial risks.

Financial Planning and Analysis

• Develop and implement comprehensive financial plans, budgets, and forecasts.
• Conduct regular financial analysis and reporting to identify trends, risks, and opportunities.
• Provide insightful financial insights to support strategic decision-making.

Team Leadership

• Lead and develop a high-performing finance team.
• Foster a positive and collaborative work environment.
• Recruit, train, and mentor finance professionals.

Skill Requirements – Essential

• Proven experience as a Group Financial Controller or equivalent role in a complex, multinational organization.
• Strong understanding of international accounting standards (IFRS, GAAP) and tax regulations.
• Experience in building finance operations and setting up financial systems and infrastructure to enable a high-growth organization.
• Advanced knowledge of financial analysis, budgeting, and forecasting techniques.
• Understanding of larger and complex business structures and groups;
• Strong leadership and people management skills.
• Excellent communication and interpersonal skills.
• Proficiency in financial software and systems (e.g., ERP, BI tools).
• A strong analytical mind with a keen eye for detail.
• A proactive and solution-oriented approach to problem-solving.
• A passion for driving financial excellence and contributing to the company’s growth.

Skill Requirements – Preferable

• Professional accounting qualification (e.g. ACCA, ACA, CPA or equivalent) with experience in a complex, multi-entity finance environment.
• Proven experience managing and developing teams and working alongside external stakeholders, including auditors, business owners, or donors, is often required.
• Additional language skills, particularly French and/or Arabic, would be an advantage.

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.
• Commitment to maintaining the highest levels of confidentiality and discretion in safeguarding sensitive information.

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.

Specific Responsibilities

Pedagogical Direction

• Guide the pedagogical decisions across the platform — input at design time, learning-efficacy judgment at eval time.
• Adapt and interpret the ALX learning framework so the platform is informed by real pedagogy rather than decorated with it.

Experimentation & Product Collaboration

• Design experiments against the platform’s core pedagogical assumptions, run with our experimentation function — for example, is the tutor guidance helping learners or crutching them?
• Assist in designing and evaluating the AI products alongside engineers, translating learning science into concrete product decisions.

Skill Requirements – Essential

• Learning science: solid grounding — you can cite and apply modern frameworks, not just intuitions from teaching.
• AI literacy: you understand what LLM-based tools can and can’t do, well enough to co-design them with engineers.
• Experiment design: hypotheses, measures, and enough methodological rigour to test learning claims honestly.
• Desirable (not required): prior EdTech product work; familiarity with competency frameworks or knowledge tracing; experience with African education contexts.

Essential Traits for Success

• Your instinct is to challenge the team’s assumptions, including the technical lead’s — that’s the job.
• You translate across disciplines without friction and enjoy being embedded in an engineering team.
• You can describe a pedagogical belief you changed your mind about, and the evidence that did it.

Role Summary

• Project A is ALX’s AI learning platform — a set of LLM products used by learners. Every one generates a stream of LLM data, and every one has hypotheses baked into it about what “working” means. The LLMOps Engineer owns the analyzer function: turning that stream into an honest answer about whether the products work. Take RAG as one example — documents must be stored accurately, fetched accurately, and fetched in the right mixture: three separate failure modes, each needing its own eval. Every product decomposes like that. This is a junior-to-mid role with a deliberate growth path: you start close to the technical lead’s designs and grow into full ownership of the function.
• You will work in collaboration with Anthropic Engineers, a cross functional  team of AI engineers, product managers and data scientists to design world class learning experiences.

Specific Responsibilities

Evaluation Suites

• Build and run eval suites per product, decomposed by failure mode, running on schedule and on every release — regression testing so nothing ships if it broke what worked.
• Keep evals cost-effective as the product line grows.

Reporting, Data & Collaboration

• Own the reporting loop — findings from evals and platform data in front of the team and stakeholders, including surfacing unintended or problematic model behaviour before learners do.
• Steward the core datasets the team depends on, including classified customer-support data.
• Partner with the AI Product Manager on instrumentation — they instrument the product, you build the evals over what is captured. This is a measurement role, not infrastructure — no model hosting or serving.

Skill Requirements – Essential

• Python & data: solid Python and a data inclination,  comfortable shaping and analysing messy LLM-generated data.
• Decomposition: the ability to look at an AI product and decompose it into success and failure metrics.
• Eval landscape: familiarity with Langfuse, RAGAS, DSPy, or similar — depth in one, awareness of the rest. These tools are learnable; we hire the fundamentals underneath them.
• Desirable (not required): experience keeping evals cheap at scale; dashboarding and reporting; classical statistics.

Essential Traits for Success

• You want to own a function, not execute tickets.
• You communicate well and like collaborating,  you will support every builder on the team.
• You can point to any project, even a small one, where you measured an AI system honestly.

Role Summary

• Project A is ALX’s AI learning platform. Under it sits a competency model and the hard algorithmic question of the whole system: given what we know about a learner, what should they do next? The Senior AI/ML Engineer designs and builds that engine. It is a probabilistic path-recommendation problem in the family of Bayesian Knowledge Tracing and Knowledge Space Theory, and it has to work from a cold start: no behavioural data yet, so the model is the prior. You encode the prerequisite structure of the domain and let it update as learners come through. We deliberately want an engineer with real modelling depth rather than a pure data scientist on a team this small, the high-value work on day zero is building, not analysing data we don’t yet have.

Specific Responsibilities

Competency Navigation Algorithm

• Own the competency navigation algorithm behind the Learner-Competency-Mapper, its design, implementation, and update dynamics as real data arrives.
• Encode the prerequisite structure of the domain and its priors, so the model performs from a cold start and improves as learners flow through.

LLM Processing & ML Growth

• Build pipelines that turn unstructured platform data into signal –  first, a constrained LLM-as-judge answering whether Chidi is effective, with model selection, eval design, and awareness of judges’ own failure modes.
• As the platform accumulates a feedback stream, builds behavioural and at-risk profiling and the models that evaluate learners for the Grader-Competency-Pulser; the role grows into genuine ML/data-science work as the data asset does.

Skill Requirements – Essential

• Probabilistic / Bayesian modelling: real depth — you have designed models from domain structure, not just fit them to data.
• Python & shipping: strong Python and the ability to ship what you design, production pipelines, not notebooks.
• Evaluation: eval design experience, or the judgment to build it fast.
• Desirable (not required): BKT/KST or psychometrics exposure; MLflow or similar experiment tracking; knowledge graphs. No prior EdTech required but useful.
• Serious probabilistic modelling of structured domains (recommenders, knowledge graphs, causal inference) is great.

Essential Traits for Success

• You reason carefully about your assumptions — in a cold-start model, bad priors compound silently, and you find that problem interesting.
• You learn unfamiliar domains fast and enjoy it.
• You can talk about a model that was wrong and how you found out.

Role Summary

• Project A is ALX’s AI learning platform: LLM products built on hypotheses about how people learn and how AI can help. Some of those hypotheses are wrong; this role’s job is to find out which ones, fast. It centres on uncertainty reduction rather than product vision. You own the Experimentation-Harness as a function — instrument the AI products well enough to evaluate them, source learners for betas (the binding constraint on the whole programme), run the ongoing beta tests, and close the loop: data reviewed, improvements shipped, next PoC out the door. UX validation is captured alongside learning validation, so we know not just whether it teaches but whether people can use it.

Specific Responsibilities

Learner Supply & Instrumentation

• Build a repeatable pipeline of beta learners, engaging the right internal teams to keep them flowing — the constraint that gates everything else, and it rewards hustle over process.
• Instrument the AI products in partnership with the LLMOps Engineer — you instrument; they build the evals over what is captured.

The Experiment Loop

• Own the experiment loop — a backlog of the team’s biggest uncertainties, experiments designed against them, cycle time measured and shrinking.
• Close the loop: data reviewed, UX and learning validation captured, improvements shipped, and the next PoC out the door.

Skill Requirements – Essential

• Product experimentation: you have run it end-to-end, hypothesis, instrumentation, decision. A/B testing or structured product testing on a live product.
• Technical fluency: you can talk concretely about instrumenting a product, read AI-eval results, and hold your own with engineers. You don’t need to code daily.
• Operator ability: recruiting and coordinating real users, running a beta programme, and wrangling stakeholders.
• Desirable (not required): hands-on eval or analytics skills; EdTech experience.

Essential Traits for Success

• You have a scientific mindset. You know what an experiment can and can’t conclude, and your favourite experiments include ones that killed an idea you loved.
• You’d enjoy the question “how would you get 50 beta learners in two weeks with no budget?” enough to start answering it in the interview.