Speech Data Scientist - Python

Description :

Responsibilities :

- Design and implement end-to-end speech analytics pipelines for production environments.

- Develop ASR engines using state-of-the-art frameworks (Wav2vec, Whisper, Deep Speech) with PyTorch or TensorFlow.

- Build and optimise speaker diarization, language identification (LID), and text post-processing systems.

- Focus on multilingual audio processing capabilities.

- Lead data selection strategies for domain adaptation and model optimisation.

- Develop and analyse objective measures for speech quality evaluation and enhancement.

- Implement speaker-conditioned personalisation techniques for improved ASR accuracy in noisy environments.

- Optimise on-device ASR models with emphasis on multilingual scenarios.

- Guide teams on best practices for model accuracy improvement and performance optimisation.

- Conduct research on advanced speech processing techniques, including neural speech enhancement.

- Develop novel approaches for complex audio scenarios and multi-speaker environments.

- Contribute to patent applications and research publications in speech technology.

- Stay current with the latest developments in transformer models, attention mechanisms, and foundation models.

- Design integration architectures for speech-to-text services and supporting technologies.

- Implement MLOps processes and CI/CD pipelines for speech models.

- Deploy and scale speech solutions on cloud platforms (AWS, GCP).

- Develop production-ready applications using Python, C++, and Java.

Requirements :

- Ph. D/M. S. /M. Tech in relevant field (Computer Science / Signal Processing) preferred.

- B. Tech/B. E in ECE, CSE, or related technical field.

- 3 to 6 years of hands-on experience in speech recognition and processing.

- Deep understanding of classical methodologies : HMMs, GMMs, ANNs, Language modelling.

- Expertise in modern deep learning techniques : CNNs, RNNs, LSTMs, CTC, Attention mechanisms.

- Strong background in digital signal processing and audio analysis.

- Proficiency with PyTorch and TensorFlow frameworks.

- Experience with transformer models (BERT, Wav2vec 2.0 Wisper).

- Knowledge of end-to-end ASR implementation and optimisation.

- Understanding of foundation models and transfer learning approaches.

- Strong Python programming skills with ML/DL libraries (numpy, pandas, scikit-learn).

- Experience with C++ and Java for production implementations.

- Proficiency in bash scripting and automation.

- Familiarity with version control (Git) and collaborative development.

- Hands-on experience with cloud platforms (AWS, GCP).

- Knowledge of containerization (Docker, Kubernetes).

- Experience with MLOps tools and CI/CD pipelines.

- Understanding of model serving and scalability considerations.