Kevin Dang Shine

Built responsive Next.js interfaces for real estate photo editing workflows, improving uploads, editing reviews, and navigation while reducing customer effort. Developed Next.js pages to support property image submission and reduced page load times by ~35%. Built Node.js/Express REST APIs for accounts, image processing jobs, and editing results, reducing integration failures by ~30%. Implemented PostgreSQL models for users, property projects, and image processing states to improve retrieval performance. Integrated an LLM-powered assistant using retrieval-augmented generation to answer workflow questions from internal documentation, reducing resolution time by ~? and improving user self-service. Implemented role-based access and security-focused authentication/authorization, and designed an admin panel for reviewing image jobs, managing work orders, and diagnosing processing issues; improved operational handling time by ~35%. Established automated API tests, frontend regression test cov

Improved Amazon Fashion and Fitness search experience by refining REST-based search page interactions and running A/B tests, reducing search-related abandonment by ~15%. Built JavaScript experimentation tools for multiple search variants, cutting setup time from days to hours. Analyzed A/B test results for filters, ranking, and navigation and produced reports that improved experiment turnaround by ~30%. Developed Java and GraphQL APIs supporting a customer service chatbot experience, improving data delivery reliability by ~25%. Integrated LLM retrieval-augmented generation into service workflows to surface relevant support information, improving resolution by ~20%. Automated deployment workflows and reduced manual release preparation by ~35%, improving release efficiency and reliability. Created reusable developer tools and automated testing utilities to reduce repetitive engineering work by ~30%. Mentored junior engineers through feature delivery and code quality improvements.

Developed Python REST APIs for Farmer’s Business Network’s agriculture-focused platform, improving response times by ~30% across core workflows such as product discovery, account access, and member purchases. Optimized PostgreSQL queries supporting product catalogs, improving database response times by ~35% during high-volume shopping. Integrated third-party vendor feeds into a data warehouse, improving product data quality by ~25% and increasing reporting reliability for marketing and business analysis. Implemented e-commerce features for promotional product filtering and purchase eligibility. Built an LLM-powered agronomy support assistant using retrieval-augmented generation over approved guidance to reduce repetitive member inquiries by ~25%. Refactored backend services and simplified API data access patterns to improve performance and maintainability. Expanded automated API testing and deployment validation, reducing production regressions by ~25%.