Data Science Engineer - IV [Voice & Speech]

Voice AI /ML Engineer

deep learning

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