AI Data Engineer

We're looking for an AI Data Engineer to join our team. You'll design, build, and deploy AI/ML data systems that power Agency Fund's platforms and partner applications, from large language model integrations and recommendation engines to data pipelines and evaluation frameworks. Your work will directly improve outcomes for millions of people served by our NGO partners.

Responsibilities:

• Design and build AI/ML evaluations and features for Agency Fund's tech platforms and partners (LLMs, NLP, recommendation systems, etc. )
• Develop robust data pipelines and evaluation frameworks for model training and inference and post-deployment efficacy.
• Work closely with behavioural scientists and researchers to translate insights into AI-powered features.
• Architect and maintain production ML systems with an emphasis on observability, performance, reliability and scalability.
• Conduct rapid prototyping and experimentation to validate AI approaches before full buildout.
• Document technical decisions and maintain engineering standards across AI components.
• Stay current with applied AI research and bring relevant advances to the team.

Requirements:

• Have 3-7 years of software engineering experience with at least 2 years focused on AI/ML systems.
• Are proficient in Python and have hands-on experience with AI/ML frameworks (PyTorch, TensorFlow, HuggingFace, etc. )
• Have built and deployed LLM-powered applications or retrieval-augmented generation (RAG) systems.
• Understand ML fundamentals: model training, evaluation, fine-tuning, and inference optimisation.
• Are comfortable with cloud infrastructure (AWS, GCP, or Azure) and have deployed models in production.
• Communicate technical concepts clearly to non-engineering audiences.
• Care deeply about building AI responsibly and with direct social benefit.

Bonus points if you have:

• Experience with multilingual NLP or working with low-resource languages.
• Familiarity with behavioural science or designing AI for behaviour change.
• Prior work in global health, international development, or social impact technology.
• Experience with data annotation pipelines and evaluation methodology for LLMs.
• Background in AI safety, alignment, or responsible AI frameworks.