Voice AI Specialist

12 hours ago


Tiruppur, Tamil Nadu, India beBeeArtificial Full time ₹ 15,00,000 - ₹ 20,00,000

Job Title: Voice AI Specialist

We are seeking an experienced Voice AI Specialist to join our team. The successful candidate will be responsible for developing and optimizing voice models, with a focus on voice synthesis, voice cloning, speech recognition, and voice transformation.

This is an exciting opportunity for someone who wants to work on cutting-edge technology and make a real impact in the field of artificial intelligence.

The ideal candidate will have:

  • A strong background in machine learning and deep learning, with experience in building and training AI models.
  • Hands-on experience with speech/audio processing and relevant toolkits.
  • Familiarity with voice model architectures and their applications.
  • Excellent problem-solving skills and the ability to work independently.

Key responsibilities:

  • Design, develop, and fine-tune deep learning models for voice synthesis (e.g., TTS, voice cloning).
  • Implement and optimize neural network architectures such as Tacotron, FastSpeech, WaveNet, or similar.
  • Collect, preprocess, and augment speech datasets.
  • Collaborate with product and engineering teams to integrate voice models into production systems.
  • Perform evaluation and quality assurance of voice model outputs.
  • Research and stay current on advancements in speech processing, audio generation, and machine learning.

Requirements:

  • Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field.
  • Strong experience with Python and machine learning libraries (e.g., PyTorch, TensorFlow).
  • Hands-on experience with speech/audio processing and relevant toolkits (e.g., Librosa, ESPnet, Kaldi).
  • Familiarity with voice model architectures (TTS, ASR, vocoders).
  • Understanding of deep learning concepts and model training processes.

Preferred qualifications:

  • Experience with deploying models to real-time applications or mobile devices.
  • Knowledge of data labeling, voice dataset creation, and noise handling techniques.
  • Experience with cloud-based AI/ML infrastructure (e.g., AWS, GCP).
  • Contributions to open-source projects or published papers in speech/voice-related domains.


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