AI Data Scientist

Design and deploy next-generation AI solutions leveraging Retrieval-Augmented Generation (RAG), embeddings, vector databases, and agentic architectures for real-world automation. Build intelligent, agent-based systems for document summarization, knowledge retrieval, content generation, and transforming unstructured data (e.g., PDFs, emails, spreadsheets) into actionable insights. Implement MCP (Model Context Protocol) and multi-agent frameworks to orchestrate complex reasoning, tool usage, and dynamic workflows across LLMs. Evaluate, adapt, and fine-tune foundation models (LLMs) for specialized domains, ensuring scalability, reliability, and measurable business impact. Develop and maintain embedding pipelines and semantic search capabilities, optimizing similarity search in large-scale vector databases. Work with multi-modal data (text, images, structured/unstructured formats) to deliver rich, context-aware GenAI experiences. Monitor and enhance model performance focusing on accuracy, latency, and robustness, including LLM evaluation frameworks for continuous improvement. Collaborate closely with data scientists to design AI-enhanced analytical workflows, ensuring that GenAI solutions seamlessly integrate with data science models and decision-making pipelines. Collaborate cross-functionally with engineers, product managers, and business stakeholders to define approaches and deliver production-ready AI systems. Stay ahead of GenAI and Agentic ecosystem advancements, rapidly prototyping new methodologies and integrating state-of-the-art tools and protocols into real-world applications.