Research Engineer - Voice and Language AI

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.

Responsibilities:

• 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.

Requirements:

• 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.