Staff Engineer @ Lightning AI |
3x Founder, 2x Acq.
Lifelong builder and 3x founder.
Started in quantitative finance, shifted into AI the past 5 years.
Now building the future of AI developments at Lightning AI.
Intros AI is a community-platform and data aggregate, aimed at automating search & discovery of individuals in online communities.
My third foundering experience, but first time raising venture capital.
Departed in early-2024 due to shifting company scope & vision.
Later acquired by Bevy (https://www.businesswire.com/news/home/20250708851996/en/Bevy-Acquires-Intros-AI-to-Launch-Engagement-Hub-to-Personalize-Community-at-Scale)
Working for Goldman Sachs Marquee - I was on the Execution team tasked with building and maintaining the Marquee trading platform. Within this team, I worked on the Commodities and FX teams, where I was one of the lead developers and shared management responsibilities pertaining to the Commodities team.
Over my 3 years on the team, I made the following key contributions:
1. Developed an orderbook to introduce Commodities Agency trading to the platform.
2. Architected and implemented asset-neutral server-side filtering of order data, and oversaw the rollout of this filtering mechanism to the Marquee platform
3. Architected and implemented an asset neutral quote-streaming grid, and integrated FX Swaps, Commod Principal, and Commod Agency assets onto the platform.
4. Automated testing of over 300 user workflows using Selenium webdriver
5. Managed an initiative to bring Commodities test coverage above 75%
6. Implemented and managed the introduction of commodities options trading to the Marquee platform
While working at Goldman Sachs, I contributed to Commodities R&D.
This team experimented with future "cutting edge" technologies that would either fail or 10x productivity in areas of the firm.
We built an event-driven, robotic-patterned trading system that would identify market movements and integrate with existing trader communication platforms.
Traders use it to automatically flag market conditions for which they manually monitor, and the firm gains data into the rational and insights of its traders.
This data is used to educate future traders and lowers the cost of churn to the firm.
