AI Solutions Architect

Code for Africa (CfA) uses technology and #OpenData to empower citizens. We give citizens actionable information for better-informed decision making and digital tools to amplify their voices, so that they can hold the authorities (both governmental and corporate) to account.

AI Solutions Architect

Position Summary

You’ll design, ship and maintain AI-enabled tools and workflows for teams across the alliance, and set the patterns others build on. This is a hands-on role: you’ll write and review code, configure integrations, test with users and support what you ship, not just produce designs or recommendations.

Your first priority will initially be Business Development (proposal and partner-intelligence workflows). From there, the work expands to research, editorial, investigative and programme operations. You’ll build on tools teams already use (Google Workspace, Slack, Airtable, CRMs) and on in-house capacity: DataLab for data engineering and scraping, the iLAB for AI-driven forensics, CivicSignal for NLP, and the wider AI Sandbox team for shared model access and infrastructure. You won’t be building the whole stack alone.

Why Join

• Be the founding hire: shape how the alliance uses AI, from the first build to the standards other teams adopt.

• Real users from day one: starting with the Business Development team, then forensic researchers, journalists, and programme staff across Africa and beyond.

• Real ownership, real support: you’ll own design decisions within agreed architecture and security boundaries, backed by in-house data and AI teams, so you’re not starting from zero.

What You Will Do

• Ship tools, workflows and lightweight interfaces end to end: take them from discovery and design through build, evaluation, rollout, training, monitoring and handoff.

• Pick the simplest thing that works: use models or agents where interpreting language or handling ambiguity adds value, and plain deterministic code or rules for anything that runs often, must give the same answer every time, or needs an audit trail. Often that means using an AI coding agent to build the automation, not to be it.

• Treat AI output as unverified until checked: build evaluations, source traceability and automated checks, and put a person where the decision matters. Assume outside content may be wrong or may try to steer the model.

• Build the function, not just the tools: set up an intake and prioritisation process, a reusable library of templates, connectors and review components, and clear rules for what stays central and what programmes own.

• Keep it maintainable: use version control and code review, write documentation others can run from, and build on shared Plus and AI Sandbox platforms and security standards, so everything works with the rest of Plus’s systems.

• Keep costs visible: estimate running costs, compare managed, open-source and in-house options on cost, security, portability and maintenance, and replace expensive SaaS where there’s a clear case.

What This Role Is Not

This is not primarily a chatbot, dashboard or research role, and it’s not an on-demand automation desk: you’ll prioritise work with the CTO and programme leads based on value, risk and potential for reuse. You won’t own data engineering, model infrastructure or organisation-wide AI policy, but you’ll work closely with the teams that do.

What You Will Build in Year One