Research Engineer Voice and AI

GreyLabs; Bengaluru Urban; On-site; INR 4,000,000 - 7,500,000; Full time

Job summary

GreyLabs AI is building a voice operating system for India’s BFSI, and seeks an experienced IC in our R&D team. You will bridge research and production, taking prototype work into reliable features deployed for enterprise use.

You will work across STT/LLM/TTS systems, fine-tune LLMs, improve ASR, and contribute to evaluation frameworks with observability for real customer deployments in multilingual Indian languages.

Qualifications

• Extensive experience building ML/NLP systems for production.
• Hands-on with LLMs: prompts, fine-tuning, evaluation.
• Experience with ASR/STT like Whisper or Kaldi.
• Proficient Python for backend services.

Responsibilities

• Improve STT/ASR transcription accuracy and latency.
• Fine-tune, evaluate, and deploy LLMs for BFSI tasks.
• Build and benchmark TTS improvements against product requirements.
• Extend and maintain prompt engineering and RAG infrastructure.

Skills

ML/NLP systems

LLMs deployment

ASR/STT techs

Python production

Backend integration

Cloud (AWS/GCP)

LangChain/HF tooling

Tools

Hugging Face

LangChain

AWS

GCP

Job description

GreyLabs AI is building the voice operating system for India’s BFSI. Our Agentic Voice AI platform helps banks, insurers, NBFCs, and fintechs automate and humanise millions of customer conversations - across sales, collections, customer service, and compliance - in multiple Indian languages.

In under two years, we’ve scaled to 50+ enterprise clients, including RBL Bank, AU Small Finance Bank, IDFC FIRST Bank, SBI Life, ICICI Prudential Life, and Motilal Oswal - processing hundreds of millions of conversations. We raised ₹85 Crores in Series A funding led by Elevation Capital with Z47, and were recognised for “Best Use of AI in Fintech” at IFTA 2025.

The Role

This is a pure Individual Contributor role within our R&D function, working across STT, LLM, and TTS systems. The mandate is the same one every research role here carries: close the distance between research and production. You'll work directly with backend engineers to take your work from a working prototype to something running reliably in front of real customers, with the observability it needs to be trusted at enterprise scale.

What You’ll Do

• Improve STT/ASR transcription accuracy and reduce latency on specific domain and language targets, within multilingual, financial-services voice data
• Fine-tune, evaluate, and deploy LLMs for defined BFSI tasks: information extraction, classification, summarisation, and compliance signal detection
• Build and benchmark TTS improvements against product requirements - quality, naturalness, latency, integration fit
• Extend and maintain our prompt engineering and RAG infrastructure for production LLM features
• Work directly with backend engineers to take your research output from prototype to deployed, observable production feature
• Run experiments and contribute improvements to our evaluation frameworks, so results are reproducible and tied to real product outcomes
• Track developments in open-source LLM and ASR frameworks and bring evidence-backed recommendations to the team

What We’re Looking For

• 5-8 years in software engineering, with meaningful depth in ML/NLP systems
• Hands-on experience with LLMs - from prompt design through fine-tuning, evaluation, and deployment
• Exposure to ASR/STT technologies: Whisper, Kaldi, DeepSpeech, or commercial equivalents
• Proficiency with ML tooling: Hugging Face, LangChain, or equivalent frameworks
• Cloud experience (AWS or GCP) for model training, deployment, and monitoring
• Comfortable reasoning through modelling and architecture trade-offs with incomplete information, and can explain that reasoning clearly to the wider engineering team
• Writes clean, production-ready Python that backend engineers can integrate and maintain
• Understands how AI/ML components fit into larger backend architectures

Strong Signals

• Has closed the gap between "this works in a notebook" and "this is running reliably in production"
• Has worked directly with backend engineers to ship an AI-powered feature
• Holds a high bar on evaluation.
• Can take a scoped but underspecified problem and turn it into a working plan
• Can explain a technical trade-off or limitation to a product or business stakeholder without losing precision

Why GreyLabs AI

A hard problem in a large market. Building accurate, low-latency, multilingual Voice AI for regulated financial institutions - across diverse Indian languages and under RBI and IRDAI compliance requirements - is technically complex and commercially consequential.

Real scale, real research problems. The STT, LLM, and TTS challenges here come from actual production load, real customer data, and the constraints of enterprise deployment. They are not synthetic.

Research that ships. At our current stage, the distance between a working experiment and a live product feature is short. Your work will reach millions of conversations.

Strong backing, proven team. Elevation Capital and Z47 are long-term partners invested in our vision. Our founders built and exited Cogno AI - they understand what it takes to build AI companies that earn enterprise trust.