Led the charge in deploying transformative AI solutions and measure financial impact, leveraging over a decade of AI expertise to address complex business challenges. With a focus on generative AI (LLMs), Agentic AI and ML, I spearhead projects such as MMM, churn reduction initiatives, and customer segmentation, delivering actionable insights that enhance organisational performance. My work includes building recipe recommendations (LLM) and sentiment analysis (ABSA) aligning data science/ AI innovations with business objectives. Developed a lean Agentic RAG system that enhances traditional RAG with a dynamic agent for pre-generation decisions and tool selection. A router prompt categorizes queries (OpenAI docs, 10-K reports, web search) to activate specific handlers. OpenAI and 10-K queries use vector database (Qdrant) retrieval and RAG. Web queries utilize a web-access API (ARES). This architecture emphasizes reasoning for intelligent, adaptable retrieval, combining structured knowledge with real-time search to enable complex multi-step logic without external agentic frameworks. By fostering robust MLOps practices and maintaining CI/CD pipelines, I ensure seamless operationalization of models while driving efficiency and collaboration. Passionate about mentoring, I empower teams to elevate their technical capabilities and embrace cutting-edge methods in AI. My mission is to harness the power of data science to create scalable, responsible, and impactful solutions that align with organizational goals and customer needs.