• Refractor PayPal's identity experience impacting 400 million users worldwide using React.js and Next.js, enhancing maintainability and performance of signin and signup flow, resulting in a 10% improvement in page loading speed through advanced rendering techniques like lazy loading, server-side rendering (SSR), and prefetching/preloading.

• Build responsive and accessible UIs with HTML and CSS, ensuring WCAG compliance and cross-browser compatibility, leading to a 20% reduction in user drop-off rates on mobile devices and a 15% reduction on desktop devices.

• Integrate frontend components with RESTful APIs and GraphQL, reducing data retrieval times by 30% and ensuring a smooth, real-time user experience by implementing optimized queries and caching mechanisms.

• Implement authentication and session management using cookies and JSON Web Tokens (JWT) for secure authentication.

• Implement unit and end-to-end testing using Vitest and Playwright, achieving 95% test coverage and excellent reliability.

• Manage source control and CI/CD with Git, GitHub, Jenkins, and Husky for automated testing and commit linting, leading to a 40% reduction in build failures in the staging environment and ensuring consistent code quality.

• Collaborate with the UI/UX team via Figma to translate designs into web interfaces, reducing redesign requests by 30%.

• Designed and maintained a full-stack project on Linux environment with both web pages and RESTful APIs that automate training and testing of 10+ deep learning models used in the logistics industry.

• Engineered web pages with asynchronous page pagination using Vue.js, resulted in a 20% reduction in page loading delay.

• Created various backend features using Flask, including web APIs, data processing and formatting, and database integration.

• Optimized the speed of processing task requests by 30% using Redis cache and waiting queue.

• Administered a PostgreSQL database on Alibaba Cloud to store more than 1000 datasets and data results.

• Conducted rigorous unit tests and integration tests using PyTest and Cypress, covering 100% of relevant source codes.

• Implemented and deployed scripts for CI/CD pipelines to maintain over 2000 lines of high-quality source code merging into major branches of AIoT Department’s Gitlab repo, ensuring smooth rollouts with minimal regressions.

• Contributed to an IoT-based device control system where customers could control the smart devices through mobile apps.

• Developed a Django backend system that receives users' HTTPS requests and communicates with IoT devices over TCP.

• Trained an LSTM-based machine learning model to accurately detect machine breakdowns in a sewing company’s production line using PyTorch. Reached 98.3% training accuracy and 97.1% testing accuracy by tuning model with cross-validation.