Data Science Engineer

As a highly experienced Voice AI/ML Engineer, your role will involve leading the design and deployment of real-time voice intelligence systems with a focus on ASR, TTS, speaker diarization, wake word detection, and building production-grade modular audio processing pipelines for next-generation contact center solutions, intelligent voice agents, and telecom-grade audio systems. **Key Responsibilities:** - Build, fine-tune, and deploy ASR models such as Whisper, wav2vec2.0, Conformer for real-time transcription. - Develop 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 methods like AHC, VBx, spectral clustering. - Design robust wake word detection models with ultra-low latency and high accuracy in noisy conditions. - 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 like PCM, Opus, -law, AAC, MP3, and containers like WAV, MP4. - 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. - 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 that champions diversity and is committed to creating an inclusive environment for all employees.,