Himanshu Goel
AI Researcher & Data Scientist | GenAI, LLMs, RAG
Delhi, India
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Himanshu Goel is an AI Researcher and Data Scientist with nearly six years of experience building and deploying production-grade AI, machine learning, and Generative AI systems across finance and healthcare. He specializes in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and document intelligence systems, with a strong focus on reliability, scalability, and real-world constraints.
His work spans the full AI lifecycle, including data analysis, feature engineering, statistical modeling, and the design of end-to-end ML and GenAI pipelines using Python, TensorFlow, LangChain, and cloud platforms such as AWS and Azure. Himanshu has led and contributed to enterprise solutions involving semantic search, regulatory document analysis, predictive modeling, and AI-powered decision support systems, delivering measurable improvements in accuracy, efficiency, and cost.
Himanshu is an active contributor to the global AI and technology community. He has spoken at AICon 2026, PlatformCon 2026, and GBRC 2026, and is an upcoming speaker at ICMSM 2026, AAIP 2026, and PMI. He is the author of "The Strategic Value of AI-Driven Physical Identity and Access Management: Establishing Cyber Physical Zero Trust in Critical Infrastructure" (IEEE) and "Stop Re-Embedding Everything: A Smarter RAG Architecture for Financial Documents" (VKTR).
Beyond speaking and publishing, Himanshu serves as a Technovation Mentor and Judge, has judged hackathons on Devpost, and has mentored 352+ learners through Great Learning. He has also served as a Session Evaluator at AICon 2026 and as a Peer Reviewer for the FPA and AAIP 2026 conferences.
Through his talks, Himanshu focuses on sharing practical lessons from building AI systems in production, covering architecture decisions, trade-offs, failure modes, and best practices for deploying trustworthy AI solutions in cloud environments.
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Building Internal AI Platforms: From RAG Pipelines to Platform Capabilities
This talk explores how organizations can evolve AI pipelines into internal platforms. It covers turning RAG systems into reusable platform capabilities that improve developer experience, governance, and scalability across teams.
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