---
title: 'Member of Technical Staff - Applied AI Research at Anuvaya Labs'
canonical: 'https://feeny.ai/job/member-of-technical-staff-applied-ai-research-anuvaya-labs-delhi-n6k9ggwjnpje'
type: 'job'
last_seen: '2026-09-12'
---

# Member of Technical Staff - Applied AI Research at Anuvaya Labs

- **Company:** Anuvaya Labs
- **Location:** Delhi, India
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-02-13
- **Last confirmed live:** 2026-09-12
- **Apply:** https://jobs.ashbyhq.com/anuvaya/191cd747-f55b-4f93-992a-cdac2f03f23b

## 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.

## About Anuvaya Labs

## Company Overview
- **One-liner**: Anuvaya Labs builds conversational AI agents that deliver professional services, starting with astrology and aiming to expand into other expert domains.
- **Entity Type**: Private (Unfunded / Bootstrapped) – has not raised any external funding rounds.
- **Headquarters**: New Delhi, India (office in Vasant Kunj).
- **Founded**: 2025
- **Founders**: Maahin Puri (CEO) and Nitesh Kumar Niranjan (CTO)

## Core Business
- **Primary Industry**: Conversational AI / Professional Services Automation
- **Target Customers**: B2C (end users seeking astrology/guidance) with eventual B2B expansion into other professional services (e.g., general medicine).
- **Mission Statement**: “We think Conversational AI agents will deliver all professional services in India.” – The company aims to make AI agents sound human-like while possessing deep domain expertise.

## Products & Services
- **Vaya** – A conversational AI agent for astrology. Users interact via natural language to receive personalised astrological insights.
- **Terra** – An open-source multi-agent SDK (built on Elixir `gen_statem`) for orchestrating stateful, interruptible, and context-aware conversations in production.
- **Rune** – An append-only knowledge graph providing long-term memory for AI agents. Captures how a user’s life evolves over time, deterministic and causal.
- **RCE (Realtime Context Engine)** – A system that feeds live, streaming context into agents so they reason on current information, not stale data.

## Market Standing
- **Valuation / Market Cap**: Not publicly available (unfunded).
- **Key Metric**: No disclosed revenue; total funding is $0 (self-funded / bootstrapped).
- **Notable Investors / Partners**: No institutional investors. The company states it “works with best investors” but no names are public; Tracxn confirms no funding rounds.
- **Growth Signals**:
  - Small team (3 employees per LinkedIn) with 25% monthly employee growth (indicating early-stage expansion).
  - Published open-source projects (Terra, Rune) attracting developer attention.
  - Strong engineering blog (inside.anuvaya.com) with deep technical content on multi-agent orchestration.

## Competitive Advantages
- **Deep Domain + AI**: Combining conversational AI with expert domain knowledge (starting with astrology) creates a moat in verticalised professional services.
- **Proprietary Infrastructure**: Owns Rune (long-term memory), RCE (real-time context), and Terra (multi-agent framework) – not just a thin wrapper on LLMs.
- **First-Principles Approach**: The team prides itself on deconstructing problems to fundamental truths, leading to unique architectural decisions.
- **Talent Density**: Deliberately small, flat team of A-players; no rush to hire.

## Strategic Focus
- **Immediate**: Scale the astrology product (Vaya) to a mature, widely used service in India.
- **Medium-Term**: Expand into other professional services (e.g., general medicine) using the same conversational AI platform.
- **Long-Term**: Become the infrastructure layer for AI-delivered professional services in India.

## Why Work Here
- **Culture**: High-obsession, craft-first environment. Work gets “questioned, broken down, rebuilt.” Not a 9-to-5 role; expect to think about problems after hours.
- **Work Model**: On-site only (hacker house in Vasant Kunj, New Delhi). No remote or hybrid options.
- **Team**: Extremely small and flat. Engineers, designers, and product folks work closely together.
- **Engineering Culture**: Heavy focus on building in public (open-source contributions, detailed blog posts). Use of technologies like Elixir, knowledge graphs, and real-time streaming.
- **Ideal For**: Someone who craves deep technical challenges, doesn’t mind ambiguity, and wants to shape the foundational architecture of a young company.

## Sources
1. [anuvaya.com](https://anuvaya.com/) – Company website, culture page, product descriptions
2. [linkedin.com/company/anuvaya](https://www.linkedin.com/company/anuvaya) – LinkedIn company profile, employee count, growth data
3. [builtin.com/company/anuvaya-labs](https://builtin.com/company/anuvaya-labs/) – Company overview, office location, career page
4. [inside.anuvaya.com](https://inside.anuvaya.com/) – Engineering blog detailing Terra, Rune, RCE, and multi-agent architecture
5. [tracxn.com/d/companies/anuvayalabs](https://tracxn.com/d/companies/anuvayalabs/__pBjcVxbTxax2dVOOGhUX9kFNcXohrWvGvWQNeqjf3gw) – Tracxn profile confirming unfunded status, founding details, and legal entity

## Other roles at Anuvaya Labs

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