Key Responsibilities • Design, build, and deploy AI/ML models leveraging LLMs and Generative AI techniques. • Develop NLP pipelines for text processing, information extraction, and conversational AI. • Implement Retrieval-Augmented Generation (RAG) frameworks to enhance AI reasoning and factual accuracy. • Fine-tune and optimize large-scale models for domain-specific use cases. • Collaborate with cross-functional teams to integrate AI models into production systems. • Evaluate and select state-of-the-art tools, libraries, and frameworks for LLM and GenAI solutions. • Conduct performance analysis, model evaluation, and continuous improvement of AI models. Required Skills & Qualifications • Proven experience as a Data Scientist or Machine Learning Engineer with a focus on Generative AI. • Strong knowledge of Large Language Models (LLMs) such as GPT, LLaMA, or similar. • Hands-on experience with Natural Language Processing (NLP) techniques and libraries (e.g., Hugging Face, spaCy, NLTK). • Proficiency in Machine Learning algorithms, deep learning frameworks (TensorFlow, PyTorch). • Experience implementing RAG systems and vector databases (e.g., Pinecone, FAISS, Weaviate). • Proficiency in Python and common data science tools (NumPy, pandas, scikit-learn). • Familiarity with cloud AI services (AWS Sagemaker, Azure AI, Google Vertex AI). Preferred Qualifications • Master's or PhD in Computer Science, Data Science, AI, or related field. • Contributions to open-source AI/ML or NLP projects. • Experience in prompt engineering and fine-tuning LLMs. • Strong problem-solving and analytical skills