Full Stack Engineer

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.