In A Nutshell
Location
On Site New Delhi, India
Job Type
Contract
Experience Level
Entry-level
Visa Sponsorship
Not Available
Deadline to apply
April 13, 2026
Opportunity to collaborate across multiple program strategy within India Country Office—all of which are leveraging AI to accelerate progress toward their strategic goals and objectives.
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.