• Design, build, fine-tune, and deploy LLMs, transformer-based NLP models, and GenAI solutions for both batch and real-time/streaming contexts. • Own all major components of ML pipelines: data ingestion, cleaning, pre-processing (structured & unstructured), embedding, search & retrieval, prompt engineering, RAG (Retrieval-Augmented Generation). • Collaborate closely with ML Engineers, MLOps, software engineering, product, compliance, legal etc., to move models from prototype to production—ensuring reliability, scalability, monitoring, and maintainability. • Define and implement evaluation frameworks: accuracy, bias, fairness, hallucination, consistency, latency; run UAT, stress-tests, drift detection. • Optimize models and pipelines for performance, cost, and efficiency. • Ensure best practices in model development: version control, repeatability, documentation, governance, and ethical AI use. • Mentor more junior data scientists; help build team skills in NLP, GenAI practices, prompt engineering, fine-tuning. • Identify new use cases; prototype innovations in GenAI/NLP; keep up with latest research and open source developments, decide what to adopt.