Data Science Specialist

About the Role:

We are seeking a highly experienced Voice AI /ML Engineer to lead the design and

deployment of real-time voice intelligence systems. This role focuses on ASR, TTS, speaker

diarization, wake word detection, and building production-grade modular audio processing

pipelines to power next-generation contact centre solutions, intelligent voice agents, and

telecom-grade audio systems.

You will work at the intersection of deep learning, streaming infrastructure, and

speech/NLP technology, creating scalable, low-latency systems across diverse audio formats

and real-world applications.

Key Responsibilities:

Voice & Audio Intelligence:

 Build, fine-tune, and deploy ASR models (e.g., Whisper, wav2vec2.0, Conformer) for

real-time transcription.

 Develop and finetune high-quality TTS systems using VITS, Tacotron, FastSpeech for

lifelike voice generation and cloning.

 Implement speaker diarization for segmenting and identifying speakers in multi-party

conversations using embeddings (x-vectors/d-vectors) and clustering (AHC, VBx, spectral

clustering).

 Design robust wake word detection models with ultra-low latency and high accuracy in

noisy conditions.

Real-Time Audio Streaming & Voice Agent Infrastructure:

 Architect bi-directional real-time audio streaming pipelines using WebSocket, gRPC,

Twilio Media Streams, or WebRTC.

 Integrate voice AI models into live voice agent solutions, IVR automation, and AI

contact center platforms.

 Optimize for latency, concurrency, and continuous audio streaming with context

buffering and voice activity detection (VAD).

 Build scalable microservices to process, decode, encode, and stream audio across

common codecs (e.g., PCM, Opus, μ-law, AAC, MP3) and containers (e.g., WAV, MP4).

Deep Learning & NLP Architecture:

 Utilize transformers, encoder-decoder models, GANs, VAEs, and diffusion models, for

speech and language tasks.

 Implement end-to-end pipelines including text normalization, G2P mapping, NLP intent

extraction, and emotion/prosody control.

 Fine-tune pre-trained language models for integration with voice-based user interfaces.

Modular System Development:

 Build reusable, plug-and-play modules for ASR, TTS, diarization, codecs, streaming

inference, and data augmentation.

 Design APIs and interfaces for orchestrating voice tasks across multi-stage pipelines with

format conversions and buffering.

 Develop performance benchmarks and optimize for CPU/GPU, memory footprint, and

real-time constraints.

Engineering & Deployment:

 Writing robust, modular, and efficient Python code

 Experience with Docker, Kubernetes, cloud deployment (AWS, Azure, GCP)

 Optimize models for real-time inference using ONNX, TorchScript, and CUDA, including

quantization, context-aware inference, model caching.

 On device voice model deployment.

Why join us?

 Impactful Work: Play a pivotal role in safeguarding Tanla's assets, data, and reputation

in the industry.

 Tremendous Growth Opportunities: Be part of a rapidly growing company in the

telecom and CPaaS space, with opportunities for professional development.

 Innovative Environment: Work alongside a world-class team in a challenging and fun

environment, where innovation is celebrated.

Tanla is an equal opportunity employer. We champion diversity and are committed to

creating an inclusive environment for all employees.

www.tanla.com