Summary
Sergey Knyazev is an applied AI/ML engineer with 12 years of experience building production-grade systems across research labs, startups, and enterprise teams, currently focused on GenAI and LLM agentic RAG systems at JPMorgan Chase. He has driven high-impact alignment work at Meta that reduced hallucinations by 100x and made models competitive with leading systems, and previously delivered CUDA-optimized computer vision solutions for embedded NVIDIA devices. His background spans bioinformatics and public health—developing novel viral RNA and phylogeny methods at the CDC—and academic ML research as a UCLA postdoc, giving him a rare blend of rigorous research, applied engineering, and domain expertise. Sergey combines hands-on model fine-tuning and dataset curation with systems-level productionization, and he’s comfortable navigating both low-level embedded development and large-scale LLM safety experiments.
12 years of coding experience
17 years of employment as a software developer
Master of Science (M.S.), Applied Mathematics and Physics, Master of Science (M.S.), Applied Mathematics and Physics at St. Petersburg Academic University of the Russian Academy of Sciences
Bachelor of Science (B.S.), Economics, Bachelor of Science (B.S.), Economics at State University of Nizhni Novgorod named after N.I. Lobachevsky (UNN)
University of California, Los Angeles
Doctor of Philosophy (PhD), Computer Science, Doctor of Philosophy (PhD), Computer Science at Georgia State University