Kaizen Voiz | Senior Software Engineer Research Development | india
4 days ago
Speech Recognition Engineer
Job Description
We are seeking a highly skilled Speech Recognition Engineer to join our team. The ideal candidate will be at the forefront of developing and deploying advanced speech recognition systems integrated with NLP, NLU, Generative AI, and LLMs to create innovative voice-driven solutions.
Key Responsibilities:
- ASR Development : Design and optimize state-of-the-art Automatic Speech Recognition (ASR) systems using frameworks like Kaldi, Whisper, NeMo, or wav2vec.
- Language Models: Fine-tune and deploy Large Language Models (LLMs) such as Open AI's GPT, BERT, or T5 for speech-to-text and text generation applications.
- NLP/NLU Integration: Develop pipelines for understanding natural language inputs, enhancing accuracy for tasks like intent recognition, entity extraction, and sentiment analysis.
- Generative AI Applications: Implement Generative AI techniques for conversational AI systems, voice assistants, and real-time transcription services.
- End-to-End Pipelines: Build and optimize scalable and real-time speech-to-text pipelines using tools like TensorFlow, PyTorch, or ONNX.
- Acoustic Modeling: Work on advanced acoustic modeling techniques, including self-supervised learning for low-resource languages.
- Cross-Functional Collaboration: Collaborate with data scientists, linguists, and product teams to deliver high-quality, user-centric solutions.
- Performance Tuning: Continuously enhance system accuracy and latency while maintaining privacy and robustness.
- Cloud Deployment: Deploy speech recognition systems on cloud platforms like AWS, Azure, or Google Cloud using services such as AWS Transcribe or Azure Cognitive Services.
Required Skills:
- Strong programming expertise in Python, C++, or Java.
- Proficiency in ML/DL frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Hands-on experience with ASR tools like Kaldi, Whisper, or wav2vec 2.0.
- In-depth knowledge of NLP/NLU frameworks: Hugging Face Transformers, SpaCy, or NLTK.
- Familiarity with Generative AI technologies and tools like LangChain, Haystack, or Prompt Engineering for LLMs.
- Experience in training and fine-tuning deep learning models for both speech and text processing.
- Strong knowledge of signal processing, acoustic features, and feature extraction techniques (e.g., MFCC, PLP, spectrograms).
- Understanding of real-time streaming technologies and APIs (e.g., Flask, WebSockets).
Preferred Qualifications:
- MS or PhD in Computer Science, AI, ML, or related fields.
- Experience working on low-resource language ASR systems.
- Familiarity with privacy-preserving AI techniques.
- Proven track record of deploying production-ready models in cloud environments.
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