Speech Engineer/Architect

Position Overview:

• Lead the development of cutting-edge speech recognition and processing systems.
• Focus on complex tasks: speaker diarization, automatic speech recognition (ASR), Sentiment/Emotion recognition, and transcription.
• Guide a team of engineers and collaborate with other departments to deliver high-impact solutions.

Key Responsibilities:

• Leadership:

  Lead and mentor a team of speech engineers, providing technical guidance and ensuring successful project delivery.

• System Design:

  Architect and design end-to-end speech processing pipelines (data acquisition to model deployment), ensuring scalability, efficiency, and maintainability.

• Advanced Modeling:

  Develop and implement advanced machine learning models for speech recognition, speaker diarization, and related tasks, utilizing state-of-the-art techniques (deep learning, transfer learning, ensemble methods).

• Research and Development:

  Conduct research to explore new methodologies and tools in speech processing, publish findings, and present at industry conferences.

• Performance Optimization:

  Continuously monitor and optimize system performance (accuracy, latency, resource utilization).

• Collaboration:

  Work closely with product management, data science, and software engineering teams to define project requirements and deliver innovative solutions.

• Customer Interaction:

  Engage with customers to understand their needs and provide tailored speech solutions, assisting in troubleshooting and optimizing deployed systems.

• Documentation and Standards:

  Establish and enforce best practices for code quality, documentation, and model management within the team.

Qualifications:

• Education:

  Bachelor's, Master's, or Ph.D. in Computer Science, Electrical Engineering, or a related field.

• Experience:

  5+ years of experience in speech processing, machine learning, and model deployment. Demonstrated expertise in leading projects and teams.

• Technical Skills:

  In-depth knowledge of speech processing frameworks like Wave2vec, Kaldi, HTK, DeepSpeech, and Whisper.
• Experience with NLP, STT, Speech to Speech LLMs, and frameworks like Nvidia NEMO and PyAnnote.
• Proficiency in Python and machine learning libraries (TensorFlow, PyTorch, or Keras).
• Experience with large-scale ASR systems, speaker recognition, and diarization algorithms.
• Strong understanding of neural networks, sequence-to-sequence models, transformers, and attention mechanisms.
• Familiarity with NLP techniques and their integration with speech systems.
• Expertise in deploying models on cloud platforms and optimizing for real-time applications.

Soft Skills:

Preferred Qualifications:

• Experience with low-latency streaming ASR systems.
• Knowledge of speech synthesis, STT (Speech-to-Text), and TTS (Text-to-Speech) systems.
• Experience in multilingual and low-resource speech processing.