Advanced Voice Model Developer

1 day ago

Kottayam, Kerala, India beBeeVoiceEngineer Full time ₹ 90,00,000 - ₹ 1,20,00,000

AI Engineers with hands-on experience in building and optimizing voice models are highly sought after.

A renowned tech company is looking for a seasoned AI Engineer to work on developing, training, and refining AI models for voice synthesis, voice cloning, speech recognition, and/or voice transformation.

Work Mode: Remote

An ideal candidate would be someone who has:

  • Developed and optimized text-to-speech models that achieved human-like voice synthesis, maintaining the unique style of voice actors across multiple languages.
  • Implemented real-time processing solutions that reduced inference time to under 1 second, enhancing user interaction and experience.
  • Managed large-scale datasets for voice cloning projects, ensuring high performance and reliability while supporting multilingual transcriptions.

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.

Required Qualifications

  • 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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