• Developed and maintained automated test cases using Java, reducing manual testing time by 40% and improved test coverage.• Created and executed API test cases using Postman and Swagger to validate endpoints, enhancing API reliability and performance.• Documented over 20 critical bugs with detailed reproduction steps and screenshots, aiding in a 20% reduction in post-release defects.• Worked with developers, product managers, and other QA engineers to gather requirements and ensure quality deliverables. Participated in weekly sprint reviews for project alignment.

• Partnered with Morgan State University, FGV University and Ripple to host an inter-college crypto trading competition using NUSwap, fostering global academic collaboration. The collaboration will be presented at the University Blockchain Research Initiatives (UBRI) Connect 2024, attended by 56 universities around the world.• Improved matching engine using Golang to efficiently handle order execution and accurately match buy/sell orders. Integrated with PostgreSQL for data management and utilized GraphQL for optimized query performance, resulting in an 80% increase in the platform’s overall performance.• Gained and applied extensive knowledge of cryptocurrency trading principles, contributing to the platform’s realistic trading simulation.

• Collaborated in a pair to develop NUSwap, the first simulated cryptocurrency trading platform for academic teaching across local universities.• Enhanced frontend interface of NUSwap, implementing mobile responsiveness and real-time updates using ReactJS (Mantine) to ensure a seamless user experience.• Configured and optimized development environment using GitHub for version control and Linux (Ubuntu) for OS, including setup of repositories, branching strategies, and integration with CI/CD pipelines

• Built a ML model using ML.NET libraries and successfully integrated model into Resume Auto-Grader POC created with C# and ASP.NET framework which reduced 80% manual grading done by module tutors• Enhanced ML Model of Aptitude Test Assessment Engine using ML.NET libraries to better assist in the selection process of the polytechnic's early admission exercise, winning MOE Innergy Award 2022 – Gold• Cleaned training data using Python and Excel which improved accuracy performance of ML Models integrated into Resume Auto-Grader and Assessment Engine by 30% - 50%• Researched and proposed new libraries of technical sources and technical features which were added to the projects, saving time needed for coding and debugging

• Collaborated with team of 4 consisting of full-timers and other intern to develop items for project demonstration (demo) as requested by client• Built an automated questionnaire with validation features and user-friendly interface using JavaScript Library independently, thus helping the company to save approximately 6,000 SGD on human capital• Built a real-time management dashboard with filtering and customization features using SAS tools, enabling the team to deliver the project demo on time• Presented progress of project demo to major client during weekly online meetings, and brought negotiation to next stage