Published 2026-01-05
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 Reputed client in Enterprise class voice solution domain. 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, Fast Speech, Wave Net, 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., Py Torch, Tensor Flow). 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.
