Research Engineer - Voice and Language AI

Research Engineer - Voice & Language AI

Bengaluru · In-office · Individual Contributor

About GreyLabs AIGreyLabs 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 RoleThis 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 DoImprove STT/ASR transcription accuracy and reduce latency on specific domain and language targets, within multilingual, financial-services voice dataFine-tune, evaluate, and deploy LLMs for defined BFSI tasks: information extraction, classification, summarisation, and compliance signal detectionBuild and benchmark TTS improvements against product requirements - quality, naturalness, latency, integration fitExtend and maintain our prompt engineering and RAG infrastructure for production LLM featuresWork directly with backend engineers to take your research output from prototype to deployed, observable production featureRun experiments and contribute improvements to our evaluation frameworks, so results are reproducible and tied to real product outcomesTrack developments in open-source LLM and ASR frameworks and bring evidence-backed recommendations to the team

What We're Looking For5-8 years in software engineering, with meaningful depth in ML/NLP systemsHands-on experience with LLMs - from prompt design through fine-tuning, evaluation, and deploymentExposure to ASR/STT technologies: Whisper, Kaldi, DeepSpeech, or commercial equivalentsProficiency with ML tooling: Hugging Face, LangChain, or equivalent frameworksCloud experience (AWS or GCP) for model training, deployment, and monitoringComfortable reasoning through modelling and architecture trade-offs with incomplete information, and can explain that reasoning clearly to the wider engineering teamWrites clean, production-ready Python that backend engineers can integrate and maintainUnderstands how AI/ML components fit into larger backend architecturesStrong SignalsHas 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 featureHolds a high bar on evaluation.Can take a scoped but underspecified problem and turn it into a working planCan explain a technical trade-off or limitation to a product or business stakeholder without losing precision

Why GreyLabs AIA 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.