AI Fellow

Responsibilities

Problem Identification & Scoping

• Identify, frame, and prioritize real-world challenges in collaboration with program teams, ensuring solutions are grounded in user needs and local realities.

Prototyping & Rapid Experimentation

• Prototype rapidly with modern LLM and AI tools in partnership with the ICO AI Team (and partners where relevant) to test feasibility, generate insights, and create lightweight demos.
• Document learnings from prototypes and translating them into actionable recommendations for scale or next steps.

Design and Deliver AI Solutions

• Support with design and testing of AI/ML models in partnership with the ICO AI Team and partners, to address challenges in global health, development, agriculture, R&D, or education.
• Conduct rigorous model evaluation and validation, ensuring accuracy, fairness, and real-world applicability.
• Prepare, curate, and manage datasets, ensuring data integrity, security, and ethical use.
• Develop high-quality, reliable software, applying strong engineering practices such as clean code, version control, and robust testing.
• Leverage large language models (LLMs), including deployment, fine-tuning, and adaptation to specific use cases.

Teaching & Capacity Building

• Support in development and delivery workshops, guides, and playbooks to enable Foundation staff and partners to use LLM tools effectively in their own work.
• Mentor program teams and local partners in applying AI responsibly, with a focus on usability and adoption in LMIC contexts.

Collaborate Across Teams and Partners

• Apply interdisciplinary problem-solving skills to translate AI research into practical solutions across multiple domains.
• Communicate technical concepts effectively to both technical and non-technical stakeholders, enabling shared understanding and informed decision-making.
• Collaborate with cross-functional teams across disciplines and geographies to co-design and deliver impactful solutions.
• Facilitate workshops and training sessions to demystify AI tools, enabling teams to directly apply solutions to their workflows.

Advancing Responsible and Ethical AI

• Embed equity, bias mitigation, and data privacy in all technical approaches in line with Govt. of India’s guidelines.
• Ensure prototypes are designed with explainability in mind, making them accessible to non-technical stakeholders.
• Application of prior academic knowledge, or professional experiences to advance the responsible and impactful use of AI in the abovementioned projects/ areas of work.

Skillset

• Education: Candidates preferably with a Bachelor’s or Master’s degree (in Computer Science, Data Science, Engineering, Applied Math, or relevant field).
• AI Technology: Proficiency in ML/AI modeling, software engineering, model evaluation, LLM deployment, and data wrangling, with evidence of applied coursework, research, internships, or early professional experience.
• AI Applications & Professional Experience: Early-career builders with demonstrated applied experience in-line with the projects mentioned above (via internships, research, or 3+ years of work), with enthusiasm and literacy to use modern AI tools safely and pragmatically (direct AI research experience not required).
• Collaborative Mindset: Strong teamwork and adaptability, with experience working cross-functionally in complex environments.
• Communication Skills: Demonstrated ability to clearly articulate technical concepts and recommendations to both technical and non-technical audiences.
• Mission-Driven Ethos: Deep motivation to apply AI responsibly for public good, with alignment to equitable development and global health impact.
• Builder and Teacher Mindset: Hands-on experience building prototypes with LLMs (e.g., using open-source tools or API-based platforms) and a willingness to share learnings openly. Ability to teach and enable others, not just deliver technical outputs.