Software Engineer

About Chariot
Chariot is a frontier AI research lab based in Delhi, building foundational AI models. We're one of the original four teams selected under the IndiaAI Mission to build models from India. We want our models to work as well in Indian languages as the best AI systems work in English.
The Role
You'll own products and systems across Chariot, from deciding what to build for users to shipping it, measuring how it performs, and improving it. That includes full-stack product development, data and evaluation pipelines, and the backend infrastructure that serves our models at scale.
You'll work directly with users and researchers to understand problems, make product and engineering decisions, and take responsibility for the result.
What you'll work on
Products, end to end. Understand user needs, decide which problems to solve, and build across the frontend and backend. Scope work into useful increments, launch, and improve based on what you learn.
Measurement. Decide what success looks like and instrument products to understand whether users are getting value. Use usage data, feedback, and failure patterns to guide what you build next.
Backend and inference systems. Design and operate services that serve our models reliably at scale. Own APIs, queues, storage, and GPU-backed inference workloads, with close attention to latency, throughput, cost, and reliability.
Data pipelines. Build and own systems that collect, clean, filter, deduplicate, and prepare large volumes of text and audio for training and evaluation.
Evaluation. Work with researchers to turn test sets and error patterns into automated, repeatable evaluations that measure model quality and catch regressions.
What we look for
Extensive experience building and operating backend systems at scale. You've owned production services under real load, handled failures, and made informed tradeoffs around architecture, performance, reliability, and cost.
Strong product sense. You can work through an ambiguous user problem, choose what matters, and explain why. You know how to scope a first version, decide what to measure, and use evidence to improve it.
Full-stack engineering ability. You're comfortable building usable interfaces and the services behind them, and carrying a feature through to production.
Strong software fundamentals, especially in Python. You write maintainable code, understand distributed systems and data infrastructure, and can debug beyond the surface.
Thoughtful use of AI tools. AI-enabled workflows are part of how you build. You know how to give tools useful context, review their output, and verify correctness. You remain accountable for the code, architecture, and user experience you ship.
Ownership beyond launch. You follow through on adoption, quality, and reliability, and change course when something isn't working.
Experience with model serving, GPU infrastructure, large-scale data pipelines, ML evaluation, speech systems (ASR or TTS), or multilingual products is a strong plus.
How to apply
Send us your resume, links to code or products you've built, and a short note on why this role interests you. Tell us about something you owned end to end: how you chose what to build, the scale and engineering challenges involved, and what you learned after launch.

Work arrangement
No