Jigyasa Grover
12-time award-winning AI lead and 'Sculpting Data For ML' author Jigyasa Grover drives rider personalization innovation at Uber after transforming Twitter/X, Facebook/Meta, Faire, and Bordo AI with large-scale ML systems. Handpicked by Google for their I/O 2024 keynote, she serves on Google's Developer Advisory Board while advising social search engine Diem and other Silicon Valley startups.
As a LinkedIn Learning instructor, Jigyasa educates thousands of professionals worldwide on cutting-edge AI-powered applications and agentic AI systems, solidifying her status as a thought leader in artificial intelligence education. As a Google Developer Expert, Women Techmaker Ambassador, and World Economic Forum Global Shaper, Jigyasa has also been featured in Forbes, Business Insider, VentureBeat, and International Business Times, and has elevated panels with Harvard University, Preston-Werner Ventures, Norwegian Business School, Humanitarian Frontier in AI, Women in Data, and more to her name.
The UC San Diego alumna has secured funding from the Canadian and Norwegian governments, the Linux Foundation, and multiple tech giants, enabling work that transcends geographical boundaries. With 200+ media features and contributions to open source recognized by Apache and Python Software Foundations, she mentors next-generation talent while shaping AI's future through advisory roles at Bezoku AI, Las Positas College, and various AI forums.
Uber
ML Tech Lead • 12x AI + Open Source Award Winner • Google Developer Advisory Board Member • LinkedIn [in]structor • Book Author • Startup Advisor / Speaker / Tech Creator • Featured @ Forbes, UN, Google I/O, and more!
Session
Large Language Models are increasingly integrated into scientific and production workflows, yet evaluation practices often remain informal and notebook-driven. This talk explores how to build reproducible, measurable, and regression-safe LLM evaluation pipelines using Python. We will examine dataset design, metric selection, deterministic evaluation harnesses, and CI integration strategies that transform LLM experimentation into disciplined, testable engineering workflows.