AI Applications Developer

Job Description Job Purpose

The AI Applications Developer will design and build an AI-powered, RAG based knowledge retrieval system under the next phase of the Platform for Development Research and Communication (PDRC 2.0). This engine will systematically convert the six Human Development volumes into high-quality, structured dissemination outputs including policy briefs, teaching case studies, executive summaries, translation-ready drafts, event notes and multimedia scripts. The role requires good understanding of Artificial Intelligence concepts, development of Large Language Models, and building production ready RAG (Retrieval-Augmented Generation) systems. The position will play a foundational role in establishing a scalable, AI-enabled knowledge infrastructure for future research outputs.

Job Outline

The Platform for Development Research and Communication (PDRC) has produced six comprehensive Human Development volumes synthesising evidence across nutrition, health, education, institutions, climate and rural prosperity. To enable systematic and efficient transformation of large research documents into structured dissemination products, the project seeks to build an AI-driven Knowledge Engine. This system will:

• Extract thematic insights across chapters
• Generate structured policy briefs
• Create case study drafts
• Develop executive summaries
• Prepare translation-ready formats
• Assist in event briefing notes
• Support multimedia scripting

The AI Applications Developer will lead the design, development, testing and deployment of this system. The successful candidate will carry out the following activities:

• Knowledge Engine Development: Design and develop a web-based, natural language query interface over the 6 PDRC volumes. Build a data pipeline that: a) Chunks, embeds, and indexes the PDF volumes into a vector store b) Retrieves relevant passages at query-time c) Generates grounded answers with source citations via an LLM (OpenAI API or open-source equivalent) d) Surfaces associated data and charts on demand. The frontend shall be built using React or an equivalent framework; the backend shall expose an API layer connecting the UI to the RAG pipeline
• Data Engineering: Develop data pipelines that enables the system to: a) Perform structured document parsing b) Generate chunks and embeddings c) Metadata generation d) Thematic clustering e) Citation preservation systems. Ensure traceability between source content and generated outputs.
• Quality Assurance: Develop test cases to validate the system against: a) Hallucination detection b) Fact-grounding checks c) Bias assessment
• Deployment: Work with other tech personnel in the department to deploy the data pipelines, backend and frontend to the production servers.
• Collaboration with Research & Communications Teams: Work closely with: a) Researchers (content structuring) b) Events Team (brief generation tools) c) Communications & Design Cluster (structured output generation)

Job Specifications Knowledge / Education:

• Master s degree in Computer Science, Artificial Intelligence, Data Science, Information Technology, or related fields.

• AI specific certification or demonstrable experience in LLM development required.

Specific Skills:

• Strong understanding of Large Language Models (LLMs).
• Experience with RAG architecture and embeddings.
• Experience working with OpenAI APIs or open-source LLMs (Llama, Mistral, etc.).
• Strong experience in programming in Python.
• Model evaluation and validation frameworks, Explainable AI.
• Working experience in frontend development using React or any similar javascript framework.

Desirable Experience:

• 2 - 5 years of experience in full stack development. Minimum 1 year in developing AI applications.
• Prior experience with public policy, social sciences, or development sector data is preferable.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.