Full Stack Engineer

Wadhwani AI Global; Delhi; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Wadhwani AI Global is seeking an engineer to build and ship production mobile and backend applications that work well offline in low connectivity environments. You will own the deployment, monitoring, and release processes while enabling internal tooling for data review and progress tracking.

You will collaborate with ML teams to turn models into usable APIs or device-ready solutions, apply data protection practices, and travel for deployment as needed.

Qualifications

• 3+ years building and shipping production applications with ownership of at least one product from first commit to live users.
• Proficiency in Python, ideally with FastAPI, and strong API design instincts.
• Strong JavaScript/TypeScript on the client side with React or React Native experience.
• Comfortable designing and querying databases and evaluating schema trade-offs.

Responsibilities

• Build and ship mobile apps for low connectivity environments with offline-first sync and local storage.
• Build and maintain backend services containerised and deployed on AWS, plus data layer.
• Own deployment, release, logging, and monitoring of the services you build.
• Create internal tools for non-technical colleagues to review data, annotate it, and track progress.

Skills

Python

FastAPI

JavaScript/TypeScript

React/React Native

API design

AWS

Database design

Communication

Offline-first

Mobile release

Data protection laws

ML models

Travel willing

Tools

AWS

FastAPI

Job description

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.

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.