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.

- 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 :

- 10+ 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.