We are seeking an experienced Generative AI Data Scientist with a strong background in LLM training, prompt engineering, and agentic AI systems. The ideal candidate will combine deep technical expertise with innovative thinking to design, train, and deploy cutting-edge AI solutions that drive business impact. You will work closely with cross-functional teams to implement and scale generative AI capabilities across the organization. LLM Development & Optimization Fine-tune and optimize foundation models (e.g., GPT, LLaMA, Claude, Mistral) for specific business and domain applications. Build scalable pipelines for model training, evaluation, and deployment. Conduct performance tuning, benchmarking, and continuous improvement of model accuracy and efficiency. Prompt Engineering & Evaluation: Develop and refine advanced prompting strategies (structured, few-shot, chain-of-thought, tool-augmented prompts). Design automated evaluation frameworks for response quality, consistency, and factual accuracy. Agentic AI & System Design: Architect intelligent, autonomous, or semi-autonomous AI agents that can reason, plan, and execute tasks using contextual knowledge. Integrate external APIs, vector databases, and retrieval systems to enhance model reasoning and context handling. Data Strategy & Engineering: Curate, preprocess, and manage large-scale datasets for generative AI model training and fine-tuning. Ensure data quality, ethical use, and compliance with data governance standards. Research & Innovation: Stay up to date with advancements in LLMs, RAG, multi-agent systems, and multimodal AI. Prototype and experiment with emerging GenAI technologies to identify potential use cases and improvements. Bachelor or Master degree in Computer Science, Artificial Intelligence, Machine Learning, or related field. Proficiency in Python, with strong knowledge of libraries and frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, LangChain, or LlamaIndex. Hands-on experience in LLM fine-tuning, prompt design, RAG pipelines, and model evaluation. Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate) and AI orchestration frameworks. Strong understanding of machine learning workflows, data pipelines, and model deployment (MLOps / LLMOps) on AWS, Azure, or GCP. Experience building or integrating agentic AI systems or multi-agent orchestration frameworks. Exposure to multimodal AI systems (text, image, and audio). Knowledge of AI safety, interpretability, .