Wadhwani AI Global (WAIG) is a next-generation AI transformation partner for governments and multilaterals across Africa, Latin America, and Asia. We combine cutting-edge AI products with deep advisory capabilities to unlock billion-dollar opportunities in health, education, and agriculture.
This is a role designed to grow. You will start with clearly defined ownership over data collection, annotation quality, and the specs behind our apps and internal tools. As the project portfolio expands, so does the scope, and we expect the person in this seat to be running products within a year. If you want to grow into product ownership rather than wait for it, this is the job.
ResponsibilitiesOwn our data collection programmes day to day: field logistics, collector coordination, progress tracking, and chasing down why the numbers look the way they do.
Run the annotation and review loop. Define what good labelling looks like, work with annotators and domain experts, and keep quality measurable.
Write the specs, user flows, and acceptance criteria for our mobile apps and internal tools from an agreed problem statement, then stay involved through build, testing, and release.
Own the feedback loop after deployment: usage, drop off, failure modes in the field, and what changes as a result.
Be the working contact for implementation partners and field teams on live pilots, covering scheduling, blockers, and what has to be true before a rollout starts.
Talk to users and partners, work out what the actual constraint is, and write the scope that engineering and ML build from.
Support grant and partnership work with product framing, scoping, and delivery narrative.
Shape how the product works here: how we prioritise, how we decide, and what we hold ourselves to.
2+ years in a product, programme, delivery, or operations role, including something you shipped or ran that had real users.
Strong written communication. Most coordination here is asynchronous and across time zones, and the written spec is usually the only spec.
Enough technical fluency to work credibly with ML engineers: you can read an API contract, reason about what is expensive to build, and hold a conversation about model limitations without needing it translated.
Comfort with data. You can define what to measure, work with a dataset, and tell the difference between a real signal and a small sample.
Willingness to travel for field work and deployment. Understanding our users means spending time where they are.
Experience in global health, education, development, or another field deployment context.
Experience running data collection or annotation operations, including working with field teams or vendors.
Exposure to grant funded delivery, including donor reporting and milestone driven timelines.
Familiarity with AI or ML products.
Exposure to data protection regimes (DPDP, GDPR, HIPAA), or to health or education data generally.
