• Own the full lifecycle of AI/ML projects, including problem framing, data acquisition, feature engineering, model and LLM selection and fine-tuning, evaluation, deployment, monitoring, and continuous improvement. • Design, build, and maintain scalable data pipelines for ingestion, preprocessing, and feature engineering, supporting both structured and unstructured enterprise data. • Implement RAG pipelines using vector databases and embedding strategies. • Fine-tune, prompt-engineer, and evaluate LLMs for domain-specific tasks. • Lead the end-to-end lifecycle of AI models from experimentation and prototyping to scalable deployment in production environments using MLOps best practices, CI/CD, and cloud platforms (AWS, Azure, GCP).