Professional Overview
I am an AI Strategy and Data Transformation Leader with over 15 years of experience helping Fortune 500 organizations translate complex business challenges into AI-enabled decision intelligence. My expertise spans Generative AI, Data Strategy, and Enterprise Transformation, with deep domain focus in Life Sciences, Manufacturing, Consumer Goods, and Energy. As a McKinsey Practice Lead in AI & Data Strategy, I’ve guided global clients in shaping enterprise AI roadmaps, implementing Responsible AI frameworks, and embedding predictive and generative models into core supply-chain and finance operations. Recently, I led an AI Proof of Concept using Small Language Models (Microsoft Phi-2, Phi-4, and Mistral) combined with LangChain RAG and Streamlit, demonstrating how lightweight models can power secure, cost-efficient GenAI solutions across supply-chain business processes. This work laid the foundation for scalable AI adoption strategies that reduce bias, accelerate forecasting, and optimize working capital. My mission is to help organizations move from AI experimentation to measurable business impact — unifying strategy, governance, and technology into a single value-driven roadmap. Core Competencies: AI Strategy · Generative AI · RAG Frameworks · Data Governance · Responsible AI · ERP (SAP IBP, S/4HANA) · Cloud (Azure, GCP, AWS) · AI/ML Enablement · Executive Storytelling · Transformation Leadership Certifications: Google Generative AI Leader | AI for Managers (Microsoft/LinkedIn) | AI Agent Fundamentals (Hugging Face) | Google Professional Data Engineer | RAG Framework Deployment (Google Cloud)
Areas of Expertise
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
