React, Python, Langchain/Langgraph, Datadog, Kubernetes, REST APIs (Confluence, Jira, Slack)Leading the full-stack development of AI-coordinated workflows using Python (Langchain/Langgraph, OpenAI) and Next.js to optimize PayPal’s software development life cycle and improve agile engineering team collaborationDeveloping an AI chatbot that automatically retrieves data from Jira Epics and Stories based on PRDs through REST APIs (Confluence, Jira, GitHub, Slack) with OpenAI, providing engineers with progress updates and recommendationsDeploying scalable AI workflows with Datadog and Kubernetes to monitor the scores of engineering workflows, ensuring 99.9% uptime and seamless service connections across 50+ systems, improving communication across teams

Python, Next.js/React, Raspberry Pi, Balena CloudLed and deployed to production a customizable receipt feature using Next.js (frontend) and Python (backend), managing thunk state transitions to trigger receipts after payment authentication and improving customer experienceUtilized a Raspberry Pi with BalenaCloud to connect Python states to a kiosk dispenser, precisely tracking soap dispensing down to 0.001 mL, reducing stock replenishment frequency by 75% and operational costs by 10%Implemented options for email, SMS, or no receipt, serving over 1,000+ customers and company partnerships

Next.js, PostgreSQL, Microsoft AzureDeveloped a full stack application to track donations and community partnerships using Next.js and PostgreSQLEngineered a PostgreSQL database and built REST APIs for a dashboard, tracking 1,000+ donations and 200+ community partnerships, improving operational efficiency by providing insights into donor and partnership activitiesImplemented featured components like filtering and navigation with React.js and single sign-on with Microsoft Azure

Excel, PowerpointNational Real Estate Pitch Competition:3rd in Nationals(50 teams, 600 participants)

Davinci Resolve

Pandas, R, Matplotlib, Scikit-learn, Seaborn, JupyterLeveraged R and Pandas within Jupyter perform a statistical analysis on datasets of 35,000+ genes and 100+ cytokines, identifying 3 potential cytokine networks associated with SARS-cov-2 vaccine rejection for 500+ patients.Developed regression models with Scikit-learn to predict patient cytokine responses, achieving an 85% accuracy in identifying biomarkers associated with SARS-CoV-2 vaccine rejection for over 500 patients.Built visualizations with Matplotlib and Seaborn to present findings, including cytokine network graphs and patient response clustering, to key stakeholders, leading to new hypotheses for research teams.