Gaurav Singh is a visionary leader and accomplished professional in Data Science, Machine Learning, and AI Cloud Technologies, with a strong track record of delivering enterprise-scale AI solutions that drive transformative business impact. With deep expertise in LightGBM, TensorFlow, Deep Learning, Large Language Models (LLMs), Generative AI, Agentic AI, NLP, Prompt Engineering, and Responsible AI, he bridges cutting-edge research with practical enterprise applications. Renowned for his Python-driven AI development, he builds intelligent systems leveraging Azure Gen AI, Databricks,Vertex AI, GCP, Synapse, and Snowflake to enable automation, accelerate decision-making, and deliver actionable insights.
Gaurav has mastered gradient boosting for tabular data, deep learning for large- scale AI, and advanced machine learning pipelines, ensuring models are robust, scalable, and production-ready through CI/CD deployment. He has successfully led high-performing Data Science teams, mentored upcoming AI professionals, and delivered measurable ROI across industries such as finance, healthcare, retail, and digital operations. A strong advocate of Responsible AI, he integrates fairness, accountability, and sustainability into every solution, while driving thought leadership in Agentic AI frameworks, autonomous ML systems, and LLM-driven innovations.
About the Technical Reviewer
Anup Das is a problem-solver who operates at the intersection of data, code, and AI. With over four years of experience, he has worked as a data scientist across diverse industries, including telecom, retail, healthcare, and finance. His expertise lies in designing systems that transform raw data and research ideas into practical, user-friendly tools.
Currently at EY, Anup focuses on integrating large language models into real-world engineering workflows. He has developed platforms that automate the generation and validation of Infrastructure-as-Code, which significantly reduces deployment errors and compliance issues. He has also designed Retrieval-Augmented Generation (RAG) pipelines to make knowledge from sources like GitHub, Confluence, and PDFs instantly searchable. Additionally, he has deployed custom Model Context Protocol (MCP) servers that enable AI agents to communicate directly with enterprise APIs and tools. His work is centered on helping teams transition from managing systems to building with them.