About Job
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.
Responsibilities
- Build and ship mobile apps for low connectivity environments, including offline first sync, local storage, and conflict resolution.
- Build and maintain backend services, containerised and deployed on AWS, along with their data layer.
- Own the deployment and running of the services you build, including release, logging, and monitoring.
- Build internal tools that let non technical colleagues review incoming data, annotate it, and track collection progress.
- Turn model work into product. Our ML engineers build speech, audio, and vision models. You make them usable behind an API or on device.
- Apply our security and data protection practices to every project you touch, and document the decisions you make.
- Build focused prototypes that let us explore new problem areas quickly.
- Establish engineering practice as you go: code review, testing, CI, and documentation.
Skills & Qualifications
- 3+ years building and shipping production applications, with real ownership of at least one product from first commit to live users.
- Strong Python, ideally with FastAPI or a comparable async framework, and sound API design instincts.
- Solid JavaScript or TypeScript on the client side, with React or React Native experience.
- Comfortable designing and querying a database, and reasoning about schema trade offs rather than accepting the first design that works.
- Practical AWS experience: you can provision, containerise, deploy, and debug a service.
- Clear written communication, since most coordination is asynchronous and across time zones.
- Offline first or intermittently connected application experience.
- Mobile release experience: Play Store, over the air updates, build distribution to testers.
- Exposure to data protection regimes (DPDP, GDPR, HIPAA), or to health or education data generally.
- Experience serving ML models, or working closely with ML teams.
- Comfort working in low resource field settings, or willingness to occasionally travel for deployment.
