Software Engineer with 3+ years of experience building scalable full-stack applications and AI-powered systems. MS Computer Science candidate at UNC Chapel Hill. Specialized in mobile development, backend APIs, RAG architectures, and MLOps pipelines. Delivered production systems serving 1,000+ users with measurable improvements in performance, reliability, and user engagement.
Languages: Python, Dart, JavaScript, TypeScript, Bash, C#, C/C++
AI & ML: LangChain, LangGraph, Ollama, Vector Databases, RAG, Computer Vision
Mobile & Frontend: Flutter, Android, iOS, Kotlin, Swift
Backend: FastAPI, Node.js, Express.js, .NET, PostgreSQL, MongoDB, REST APIs
Cloud & DevOps: Git, AWS, GCP, Docker, Kubernetes, CI/CD, GitHub Actions
Migrated legacy mobile application to Flutter while restructuring backend APIs in .NET, improving cross-platform stability. Reduced crash rate from ~3% to less than 2% by fixing race conditions between async Flutter UI state and backend responses. Shipped frontend and backend features contributing to 22% increase in weekly active user engagement. Evaluated and integrated third-party SDKs, reducing integration rework by 29%.
Developed cross-platform mobile applications using Flutter, contributing to ~30% increase in active client usage. Integrated pretrained computer-vision models for automated dental feature detection with inference optimization. Designed backend REST APIs using Node.js, Express.js, and PostgreSQL, improving request processing time by ~40% with 99.8% uptime. Built CMS tools and payment integrations, reducing content update time by ~50% and increasing transactions by ~15%. Promoted within 8 months.
Engineered Python-based reproduction system automating virtual environment provisioning, dependency pinning, and dataset versioning. Reduced developer setup time to under 1 minute. Built fully automated, idempotent execution pipeline with checkpoint detection and modular stages.
Architected serverless RAG-based chatbot using FastAPI, LangChain, Gemini, and Upstash VectorDB for contextual Q&A. Reduced p95 response latency by ~40% through optimized document chunking and vector retrieval. Automated vector store generation and deployment using GitHub Actions, reducing manual deployment effort by 58%.
Improved location-based search performance by 43% using custom indexing and in-memory caching. Implemented image compression reducing storage footprint by ~60% without quality loss. Built Flutter app with Firebase authentication, reducing unauthorized access attempts by ~30%.
Engineered Node.js REST APIs and SQL database architecture, improving data access efficiency by ~40%. Integrated Apple HealthKit and Google Health Connect in Flutter, improving nutrition and activity tracking accuracy by 43%.
Developed Flutter-based web application for interactive healthcare escape room learning experience. Designed custom game update loop reducing gameplay latency by ~40% under concurrent load. Platform used by 1,000+ students across UNC, UIC, and Monash University, improving engagement by ~30% and learning retention by ~25%.
Master of Science, Computer Science - University of North Carolina Chapel Hill (August 2024 - May 2026)
Research: Built Flutter-based training platform for UNC Eshelman School of Pharmacy used by 1,000+ students.
Coursework: Software Development, Machine Learning, Robotics, Game Development, Data Structures & Analysis
