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.