AI/ML Engineer

Responsibilities:

• Design, develop, and optimize ML models for NLP, CV, and data-driven applications.
• Work with transformer architectures, multimodal AI models, and generative AI techniques.
• Speech: Wav2vec, WavLM, Token classification, TDNN,
• Implement ML pipelines, including data preprocessing, feature engineering, training, evaluation, and deployment.
• Design and implement a Speech LLM-based virtual assistant
• Develop and maintain Python code for audio capture, encoding, modality fusion, LLM interaction, and text output.
• Integrate and configure pre-trained models for audio encoding, LLM and audio decoding.
• Stay up-to-date with the latest research and advancements in Speech LLMs, audio processing, and related fields.
• Document the system design, code, and experimental results Architect scalable, production-ready ML systems integrating MLOps best practices.
• Set technical direction, ensuring best practices in AI research, model deployment, and engineering scalability. 

Required Skill:

• 5+ years of experience in ML, AI, or deep learning, with hands-on experience in NLP, speech recognition(ASR) and text to speech (TTS)
• Knowledge of multimodal learning and techniques for fusing audio and text information.
• Solid understanding of audio processing concepts, including audio feature extraction, signal processing, and acoustic modeling.
• Experience with fine-tuning transformer models and developing training pipelines.
• Strong programming skills in Python, with experience in deep learning frameworks such as PyTorch or TensorFlow
• Familiarity with Docker, Kubernetes, Airflow, and serverless computing.
• Ability to build automated model training, evaluation, and monitoring pipelines.