AI / ML Engineer

You will design, train, fine-tune, and deploy production-grade speech and voice AI models. This is not an API-integration-only role; you will work deeply with TTS/STT models, own the ML lifecycle, and improve latency, accuracy, and robustness in production.

The candidate will have responsibilities across the following functions:

Model Development and Fine-Tuning:

• Train and fine-tune STT/TTS models (e. g., Whisper, Conformer, VITS, HiFi-GAN, or similar)
• Build and optimise neural vocoders and acoustic models for natural, low-latency speech synthesis.
• Experiment with adapter-based approaches (LoRA, fine-tuning recipes) and distributed training on multi-GPU setups.

ML Pipelines and Evaluation:

• Design scalable ML pipelines for data preprocessing, feature engineering, training, evaluation, and deployment.
• Create evaluation frameworks to measure WER, entity F1 latency (P95/P99), MOS, PESQ, and conversational quality.
• Automate logging and benchmarking (e. g., MLflow) and maintain comparisons against commercial baselines.

Real-Time Voice Systems:

• Architect streaming speech pipelines targeting sub-300ms end-to-end latency for live assistant interactions.
• Integrate with telephony/real-time communication stacks and optimise for interruption handling, turn detection, and VAD.

Collaboration and Ownership:

• Work closely with backend, platform, and product teams to ship AI features into production.
• Take ownership of model performance, data quality, and continuous experimentation.

Requirements:

• Strong Python; experience with PyTorch/TensorFlow and deep learning for speech/audio.
• Hands-on experience training/fine-tuning STT/TTS models (Whisper, ESPnet, NeMo, Kaldi/K2 SpeechBrain, etc. ).
• Experience building ML pipelines, evaluation frameworks, and deploying models on the cloud (GCP/AWS).
• Understanding of real-time constraints, latency optimisation, and production ML systems.

Nice-to-Have:

• Experience with Indian language datasets and multilingual speech models.
• Familiarity with LiveKit Agents, Pipecat, or similar real-time voice frameworks.
• Background in fintech, BFSI, or regulated domains.