15h Left Lead AI Engineer

3 weeks ago

India Prasha Consultancy Services Private Limited Full time
Immediate or early Joiners preferred.

A US Based IT MNC is looking for a seasoned AI Engineer with hands-on experience in building and optimizing voice models, for one its Client. Candidate will be working 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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