• Collaborate with a partner in developing a project on the integration of Random Matrix Theory in Modern Portfolio Theory to improve the Efficient Frontier and risk analysis using python• Research and discuss various statistical, and quantitative strategies in algorithmic trading such as Statistical Arbitrage, Monte Carlo Simulations, etc. with fellow developers to implement into different projects• Attend weekly meetings to learn various programming techniques and algorithms such as machine learning regressions, backtesting, and data manipulation/visualization using pandas, numpy, and matplotlib

• Oversee project management for international automotive engineering competition team of 35+ members by assigning tasks to 5 different sub-teams • Coordinate team logistics with executive board of 4 to optimize workflow and communicate sponsorship deals with companies such as Polaris, Dunkin, and PTC• Mentor team of 20+ new recruits through PowerPoint presentations and live demonstrations to help them acclimate to new team dynamics

● Direct a sub-team of six engineers in designing, manufacturing, and testing components of engine, transmission, gearbox, differentials, driveshaft, cv axels, and wheel hubs for a vehicle meant to adhere to particular competition standards● Facilitate design reviews by listening to ideas from team members and using data to formulate final recommendations ● Delegate various roles and responsibilities to team members via Slack and onsite meeting communications to maximize productivity and improve workflow

● Respond to thousands of daily service request and incident tickets received from ServiceNow and walk-ins from students and staff across campus ● Create unique problem-solving procedures that involve diagnostics, tests, and evaluations to meet client demands on an individual basis● Guide clients in how to use various software such as Adobe and Office Suite products, printers, and computers to optimize learning and teaching operations● Advise other full-time staff with challenging tickets when called upon to solve issues more efficiently and learn collaborative team processes

• Developed and implemented robust evaluation frameworks for assessing the accuracy and fulfillment of large language model (LLM) outputs, with a specific emphasis on API integration, parameter validation, tool utilization, and comprehensive edge case management.• Refined and enhanced prompt engineering strategies by systematically analyzing and optimizing tool function calls and query parameters, significantly improving the LLM's contextual accuracy and effectiveness, particularly in handling complex edge cases.• Engineered high-quality, optimized code completions for coding prompts, delivering precise, technically sound examples that served as strong learning models for AI, directly contributing to enhanced output reliability and performance.