Isaac Kogan is a full-stack engineer with five years of hands-on experience building scalable data pipelines, enterprise web apps, and generative AI solutions. He combines practical reverse-engineering skills—demonstrated by large-scale TikTok data collection and a Python TikTokLive library contribution—with production-grade backend work in FastAPI, Qdrant-powered semantic search, and React/TypeScript frontends. Isaac has delivered AI chatbots and embedding-driven search at York University and created robust integrations for marketing analytics at Euler Stream and Loyalift. Comfortable operating across research and product contexts, he’s shipped monitoring stacks (Grafana/Prometheus), automated token-reverse engineering for web scraping, and well-documented open-source tooling. Based in Toronto, he pairs a biology background with a hacker mindset—“making messes & breaking codebases”—to solve messy data problems and turn reverse-engineered insights into reliable systems.
5 years of coding experience
2 years of employment as a software developer
Bachelor of Science - BS Biology General, Bachelor of Science - BS Biology General at York University
The definitive Python library to receive livestream events (comments, gifts, etc.) in realtime from TikTok LIVE.
Role in this project:
Back-end Developer
Contributions:47 releases, 2 reviews, 389 commits in 11 months
Contributions summary:Isaac's contributions primarily focused on expanding the functionality of a Python library for receiving livestream events from TikTok LIVE. They added support for share events, along with examples and documentation. The changes also included modifications to the client and event type definitions, and the setup file, reflecting updates to the library's core features and dependencies.
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Isaac Kogan - Full Stack Engineer - Contracting at Euler Stream