Required: minimum requirements include:

• 4+ years building and shipping software, with meaningful hands-on experience building AI-powered products or systems
  
• Fluency in Python and TypeScript
  
• Demonstrated experience designing and building agentic AI systems: multi-step task execution, tool use, memory, planning, and error recovery
  
• Strong context engineering instincts: you think about the full information architecture a model needs to be useful, not just how to phrase a prompt
  
• A systematic approach to evals: you design for measurability, not just intuition, and you know how to tell whether an AI feature is actually working
  
• Familiarity with the broader AI ecosystem: open-source tooling alongside commercial APIs and nonprofit access programmes from leading labs
  
• Strong system design instincts around AI: you think about latency, fallbacks, cost, and reliability, not just model quality
  
• Sound judgement on responsible AI: bias, fairness, transparency, and the limits of what a model should be asked to do
  
• The ability to communicate clearly across the room: to an engineer debugging a pipeline and to a journalist or funder asking what it all means
  
• Fluency in English
  
• A degree in Computer Science, Engineering, or a related field — or equivalent experience you can point to through your work and portfolio
  

Preferred: candidates who are able to demonstrate the following will have an advantage:

• Experience deploying open-source LLMs in production environments
  
• Existing relationships or experience working with AI lab programmes: Anthropic for Startups/Nonprofits, OpenAI for Nonprofits, Google.org AI access, or similar
  
• Familiarity with vector databases, embedding models, and knowledge graph approaches
  
• Experience with multimodal AI systems
  
• Background in containerisation and cloud infrastructure (Docker, Kubernetes, cloud-hosted model deployment)
  
• Experience in civic technology, investigative journalism, international development, or human rights contexts
  
• Experience with multilingual NLP, particularly for low-resource or African languages
  
• Fluency in French, Arabic, KiSwahili, or another major African language
  
• Experience working across international, cross-cultural technical teams
  

Language and Location Requirements:

• Location: Fully remote — open to candidates anywhere in the world, with a preference for those based in Africa
  
• Languages: English required; French, Arabic, KiSwahili, or any other major African language is a significant advantage
  

• Navigate the AI model landscape
  

  • Make and maintain principled decisions about when to use open-source models, when to leverage frontier models through nonprofit partnerships, and how to architect systems that avoid lock-in either way
    
  • Cultivate relationships with leading AI labs such as Anthropic, OpenAI,and others, staying close to how their technology, access programmes, and priorities are evolving
    
  • Monitor the broader ecosystem continuously, and bring the right capabilities to CfA’s work before partners and peers fall behind
    

• Engineer context, not just prompts
  

  • Design the full context that makes models useful: system instructions, retrieval strategies, memory architecture, tool outputs, conversation state, and structured reasoning chains; not just individual prompts
    
  • Build and maintain context engineering frameworks, agent templates, and workflow orchestration tools that the wider team and partner organisations can use without deep AI expertise
    
  • Create domain-specific AI assistants grounded in curated, high-quality knowledge bases, making specialist knowledge accessible and actionable at scale
    

• Design and run evals
  

  • Build evaluation frameworks that give the team genuine confidence that AI systems are working as intended, not just anecdotally, but measurably
    
  • Treat evals as a first-class engineering discipline: defining what good looks like before building, not after
    
  • Identify failure modes proactively, particularly in African linguistic and cultural contexts where standard benchmarks often fall short
    

• Build agent systems that do real work
  

  • Design and develop AI agents capable of planning, executing multi-step tasks, using external tools and APIs, handling errors gracefully, and operating with appropriate degrees of autonomy
    
  • Move the team beyond single-turn interactions toward systems that can reason, retrieve, act, and self-correct across longer workflows
    
  • Apply agentic thinking to how the team itself works; using AI-assisted development, automated pipelines, and agent tooling to move faster and build better across the portfolio
    

• Build and ship AI-powered products
  

  • Design and develop AI features across CfA’s platforms, from RAG systems and agentic pipelines to tool integrations and multimodal applications
    
  • Collaborate with product managers and designers from the start of a feature, not the end to turn user needs into sound technical decisions and technical possibilities into experiences people can actually use
    
  • Own the full cycle from prototype to production, including the unglamorous parts: versioning, output testing, edge case handling, and knowing when to ship and when to go back
    

• Drive responsible AI practice
  

  • Embed bias detection, ethical review, and human rights considerations into how CfA builds and deploys AI; particularly in African linguistic, political, and social contexts
    
  • Develop clear documentation and governance protocols that ensure accountability and auditability across the portfolio
    
  • Represent CfA’s AI thinking externally: in publications, partnerships, conferences, and peer networks
    

• Build capability across the organisation and beyond
  

  • Grow CfA’s internal AI literacy across technical and non-technical colleagues
    
  • Support partner organisations such newsrooms, civil society groups, and researchers, through direct technical guidance and capacity building
    
  • Stay closely connected to the global applied AI community, bringing relevant advances back into CfA’s work