Ali Asaria is a seasoned entrepreneur and software engineer with 16 years of experience building consumer and retail technology from code to boardroom. He co-founded Transformer Lab and previously led Tulip as CEO for a decade, where he focused on next-generation retail platforms and later took on governance roles including Board Chair and advisor positions. His technical roots trace back to early work at Research In Motion and Microsoft, blending low-level product engineering with modern full-stack web development—evident in hands-on open-source contributions like Scrumblr where he improved UI/UX and real-time interactions. Ali combines product instincts with operational leadership, having founded Canada’s largest online health and beauty retailer, Well.ca, and served on influential boards including Communitech and a Canada Excellence Research Chairs selection panel. Based in Toronto and trained in Computer Engineering at the University of Waterloo, he brings a rare mix of startup grit, technical craftsmanship, and public-facing stewardship. An often overlooked strength is his enduring habit of shipping front-end polish himself, keeping a direct connection to the user experience even as he leads organizations.
16 years of coding experience
20 years of employment as a software developer
Computer Engineering, Computer Engineering at University of Waterloo
Collaborative Online Scrum Tool Using Websockets, Node.js, jQuery, and CSS3
Role in this project:
Full-stack Developer
Contributions:88 commits, 25 PRs, 27 pushes in 9 years 11 months
Contributions summary:Ali primarily contributed to the front-end development of the Scrumblr tool, making changes to the UI and its behavior. They implemented new features such as sticky cards and color changes. The user also addressed bug fixes, including issues with the drag-and-drop functionality, and incorporated minor improvements to the user interface. Additionally, they made updates to the demo and deployment scripts.
Open Source Application for Advanced LLM Engineering: interact, train, fine-tune, and evaluate large language models on your own computer.
Contributions:5 releases, 58 reviews, 52 PRs in 1 year 2 months
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