Member of Technical Staff - Applied AI Research

Job description

## About Anuvaya We think conversational AI agents will deliver all professional services in India. We started with astrology. We're a small group of engineers, designers, and product folks building at the intersection of conversational AI and domain expertise. Making an AI agent sound human-like is hard. Making an AI an expert in a domain is also hard. We're doing both together. We're backed by Accel, Arkam Ventures, and Weekend Fund. ## The Role Frontier models are incredible at English. They're not incredible at Indic languages and our users speak Hindi, Hinglish, Tamil, Telugu, and a dozen others. Our agent needs to be fluent, accurate, and domain-expert-level across all of them. The Applied AI Research team owns two problems. First: finetuning models for our use case improving accuracy, fluency, and domain understanding in Indic languages where foundation models fall short. Second: making those models work reliably in production prompt engineering, context management, retrieval strategies, and the systems that turn a capable model into a domain expert. You'll bridge the gap between research and production. Some weeks you're running finetuning experiments to improve Hindi response quality. Other weeks you're redesigning how context flows through a multi-turn conversation. The through-line is the same: make the model better at doing what our users need, in the language they think in. ## What You'll Do - Finetune models for Indic language performance improving fluency, accuracy, and domain understanding in Hindi, Hinglish, and other Indian languages - Build and manage finetuning pipelines data curation, training runs, evaluation, and deployment of fine-tuned models - Work with the team on prompt engineering and context management designing how the model receives and reasons over information across multi-turn conversations - Design retrieval strategies that get the right domain data to the model at the right time - Run experiments on model behavior how finetuning, context structures, prompt formulations, and tool designs affect output quality across languages - Collaborate with Evaluation to measure what actually matters especially for subjective, language-dependent quality - Stay current with frontier model capabilities and figure out how to exploit new features the day they ship ## What We're Looking For - Experience with LLM finetuning you've trained or fine-tuned models, managed datasets, and evaluated results - Understanding of Indic NLP challenges tokenization, code-switching (Hinglish), script diversity, and where current models fail - Experience with LLMs in production prompt engineering, context management, retrieval-augmented generation - You understand the difference between a demo and a production system you've fought with context windows, hallucinations, and inconsistent model behavior - Strong engineering skills you ship code, not just papers. Your research runs in production. - Experimental rigor you design experiments, control variables, and know when results are significant ## Bonus - Experience finetuning on Indic language data or multilingual corpora - Experience with Claude, Gemini, or other frontier model APIs at depth - Familiarity with our stack: Elixir, TypeScript/Bun, PostgreSQL, NATS - Published work or substantial projects in applied NLP, multilingual models, or knowledge-grounded generation - You've read our whitepapers (Realtime Context Engine, Context Splitting) and have thoughts on what we got wrong ## How We Work We care about craft obsessively. Your work gets questioned, pulled apart, and rebuilt not because we're harsh, but because everyone here holds each other to a standard most places don't bother with. We work out of a hacker house in Vasant Kunj. We strongly encourage everyone to be in office. If that sounds like the only way you'd want to work [let's talk](mailto:founders@anuvaya.com).