I develop and maintain Liftoff’s core real-time autobidding systems, handling over 3 million requests per second to optimize ad performance at scale. My work involves building high-performance, low-latency systems that drive efficient ad bidding. As well as collaborating closely with Machine Learning Engineers to integrate predictive ML models and statistical analysis techniques to enhance our bidding strategies with data-driven decision-making.

Contributed to the maintenance, enhancement, and modernization of low-level storage

management components within the IBM Mainframe’s operating system z/OS, used by 70% of Fortune 500 companies.

Designed and developed new functionality requested by customers and offering management for the Virtual Storage Access Methods component of z/OS. Investigating and servicing technical issues encountered by customers and L2 technical support. Working alongside function and system testers to debug and resolve technical defects.

Facilitated 1 unit workshop classes which tutored students in mathematics varying from Precalculus to Multivariable calculus.

Enhanced the application responsible for managing the active directory, machine registry databases, and phone systems as well as provided hardware and software support for 50+ department staff.

Assisted in the migration from a legacy email server to Microsoft 365 Exchange.

Worked in the Fare Collection engineerings team to build a Java application which collects and process Linux CentOS system logs. The logs were stored in a PostgreSQL database and presented via web interface which can be queried for specific information.