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.