The Senior Officer, AI Innovation (Health) is a critical role at the intersection of technical innovation and health impact. You will identify early-stage AI bets relevant to India’s health priorities, rapidly prototype and proof-of-concept them to test relevance and feasibility. This is a highly collaborative position involving close engagement with Program Strategy Teams (PSTs) at the foundation, government counterparts, and a broad set of innovation ecosystem stakeholders, including the private sector across India.

Responsibilities

1. Frontier Scouting & Early Bet Identification

• Continuously scan the global and Indian AI landscape – small and purpose-specific models, multi-agent frameworks, alternative training and inference architectures, AI-enabled medical imaging and diagnostic tools, and energy-efficient, edge-deployable hardware among others – for relevance to health priorities.
• Identify and prioritize early-stage AI innovations and emerging technologies with credible potential to address unmet needs in Maternal Newborn Child Nutrition Health (MNCNH), nutrition, diagnostics and imaging, and connected care.
• Build and maintain a live pipeline of candidate “bets,” assessing clinical relevance, technical maturity, feasibility, and strategic fit ahead of any investment in prototyping.

2. Proof-of-Concept & Rapid Prototyping

• Design and run lightweight proof-of-concept pilots that test a new AI idea’s clinical relevance and fit to a specific health use case – such as AI-assisted imaging, point-of-care diagnostics, or clinical decision support – quickly and at low cost.
• Translate promising research and academic innovations into testable prototypes, working with clinical experts, technical teams, health program leads, and external innovators.
• Develop concept notes, demo site plans, and pilot designs structured to generate clear clinical and technical evidence on whether an idea is worth carrying forward.

3. Innovation Ecosystem Engagement

• Engage with ecosystem partners across academia, clinical research institutions, industry, and civil society to frame problems, match solutions, and identify promising early bets for the responsible use of AI in health.
• Build and sustain relationships with startups, research labs, academic medical centers, and academic institutions (e.g. IITs, IISc, AIIMS, etc.) to surface new ideas – including AI imaging and diagnostic tools – and bring emerging capabilities into the Foundation’s pipeline.
• Represent the Foundation in ecosystem convenings, and innovation forums to identify talent and ideas early.

4. Technical Evaluation & Evidence Generation

• Partner with the Measurement, Learning and Evaluation (MLE) team and clinical advisors to evaluate prototypes against health-specific benchmarks – clinical accuracy, safety, bias, and real-world feasibility – before any handover decision is made.
• Develop and apply evaluation frameworks suited to early-stage testing of clinical AI tools (e.g., imaging models, diagnostic algorithms), distinct from the rigor required at scale.
• Document learnings from each prototype transparently, including what didn’t work, to sharpen the pipeline of future bets.

5. Structured Handover to Scaling

• Package validated concepts – evidence, learnings, partner relationships, and technical specifications – into clear handover materials for the AI Applied sub-team.
• Make explicit go/no-go recommendations on which prototypes merit further investment, based on evidence generated during proof-of-concept.
• Support a structured transition period with the Scaling team to ensure context and partner relationships carry forward without loss of momentum.

6. Internal Capacity Building

• Working closely with ICO program teams, share emerging AI trends, credible new partners, and prototype learnings to enhance collective AI literacy across the Foundation.
• Contribute to an internal learning series and shared resources documenting prototyping archetypes and early-bet evaluation approaches.

Skillset

• Education: MD/MBBS, clinical/biomedical degree, or equivalent; combined with formal training/experience in bio-medical informatics, data science, or related field.
• Experience of working at the intersection of clinical practice or biomedical research and AI/ML, with direct exposure to how clinical workflows, diagnostics, and care delivery actually function in low-resource settings.
• AI Domain Experience: Hands-on familiarity with health AI applications such as medical imaging and radiology AI, diagnostic algorithms, clinical decision support tools, or predictive models, including their evaluation, validation, and real-world deployment challenges.
• Academic & Research Credibility: A track record of academic, peer-reviewed, or applied research work in AI for health – publications, clinical trials, or validation studies – that lends technical and clinical credibility when engaging research institutions and academic medical centers.
• Prototyping Mindset: A demonstrated track record of identifying and testing new ideas quickly – someone energized by early-stage experimentation rather than long-cycle program management.
• Collaborative Mindset: Strong teamwork and adaptability, with experience working cross-functionally and handing off work cleanly to downstream teams.
• Communication Skills: Demonstrated ability to clearly articulate technical and clinical 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.
• AI for Evidence Generation: Demonstrated ability to use AI effectively for research, data analysis, documentation, and communication.

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