Machine Learning Engineer - Audio (Onsite, Lahore, Remittance Salary)

Requirements:

• 4+ years of experience in Machine Learning Engineering, with a focus on audio or speech processing.
• Strong expertise in TTS systems and voice cloning (e.g., Tacotron, WaveNet, VITS, diffusion-based models).
• Proficiency in audio processing libraries such as Librosa, FFmpeg, and SoundFile.
• Familiarity with open-source models and frameworks like Hugging Face, ESPNet, and Fairseq.
• Solid understanding of MLOps tools such as Triton Server, Docker, and Kubernetes.
• Advanced Python programming skills with experience in frameworks like PyTorch and TensorFlow.
• Hands-on experience developing APIs using Flask, FastAPI, or similar frameworks.
• Familiarity with cloud platforms like AWS, GCP, or Azure.

Responsibilities:

• Train, fine-tune, and deploy open-source TTS (Text-to-Speech) and voice cloning models.
• Experiment with state-of-the-art architectures, optimizing for quality, latency, and efficiency.
• Implement advanced audio processing techniques to achieve high-fidelity outputs.
• Work on tasks like speech enhancement, feature extraction, and audio transformation.
• Leverage frameworks such as Librosa, PyTorch, TensorFlow, and other DSP tools.
• Design and deploy scalable inference solutions using tools like Triton Server.
• Build and manage automated pipelines for data preprocessing, training, and deployment.
• Optimize model serving and inference performance for real-world use cases.
• Develop robust APIs or services to integrate ML models into production environments.
• Collaborate with backend and frontend teams to ensure seamless integration.
• Create and maintain data pipelines for large-scale audio datasets.
• Ensure efficient workflows for data processing, annotation, and management.
• Keep abreast of advancements in ML and audio processing technologies, tools, and frameworks.
• Explore emerging tools for voice synthesis and cloning, applying them to enhance projects.
• Bachelors or Masters degree in Computer Science, Electrical Engineering, or a related field.
• Relevant work experience may substitute for formal education.