Lê Văn Phúc
Full-stack Developer
A full-stack web developer with 3+ years of experience building scalable web applications end to end — from NestJS & GraphQL backends to Next.js and React frontends. I enjoy crafting real-time features, clean APIs, and polished interfaces for products people use every day.
Contact Me
About Me
I am a full-stack developer with 3+ years of experience building production web applications across property-management and e-learning platforms. I work end to end — designing GraphQL APIs and data models with NestJS, Prisma, and PostgreSQL, and building responsive interfaces with Next.js, React, and TypeScript.
I'm especially drawn to real-time and data-heavy features such as live messaging, video rooms, scheduled automations, and complex data grids. Outside of work, I build side projects spanning IoT devices, webhooks, and messaging systems to keep exploring new technologies.
Personal Details
- [email protected]
- Ho Chi Minh City, Vietnam
- www.continuetostop.io.vn
- Open to new opportunities
- Open to learning new technologies
Work Experience
Full-stack Developer — S&T Properties Inc
Feb 2024 – Present
Core contributor to a large-scale property-management platform — a NestJS + Next.js system for managing properties, reservations, and guest communication.
- Built backend features for reservations, properties, and guest messaging/conversations on a NestJS + GraphQL + Prisma/PostgreSQL stack.
- Developed synchronization with a third-party channel manager, scheduled cron jobs, and data-transformation pipelines for automated property and reservation maintenance.
- Built the admin web app with Next.js, Chakra UI, Redux Toolkit, and Apollo/GraphQL — including data grids, detail views, checklists, forms, and internationalization.
- Integrated third-party services for SMS, push notifications, email, authentication, full-text search, real-time video/messaging, and background job queues.
- Set up deployment for internal apps with CI/CD pipelines, Docker, and environment/CORS configuration.
Full-stack Developer — Innoland Solutions
Feb 2023 – Jan 2024
Full-stack developer at a custom software development company, building an e-learning platform offering online courses, live video classrooms, blogs, and community discussion, with a NestJS + GraphQL backend and Next.js / React frontends.
- Built backend APIs for courses, blogs, comments, and user management on a NestJS + GraphQL + Prisma/PostgreSQL stack.
- Implemented real-time features — live video classrooms (WebRTC) and chat/messaging — using WebSockets and GraphQL subscriptions.
- Developed the public-facing site with Next.js, Chakra UI, Redux Toolkit, and Apollo Client, plus an internal admin panel with React, Ant Design, and Handsontable.
- Integrated social login (Google/Facebook), email, push notifications, and cloud file storage.
Personal Projects
Enterprise SSO / Identity Provider
A multi-tenant SAML 2.0 Single Sign-On system — a custom Identity Provider (IdP) plus a sample SaaS application that signs in through it. Features X.509 certificate management, organization-level tenancy, and TOTP-based multi-factor authentication.
SMS Message System
A full-stack real-time messaging system spanning an ESP8266 device, a backend, and a web dashboard. The firmware publishes data over MQTT, the NestJS API relays it through WebSockets and persists it in PostgreSQL, and the Next.js dashboard lets users send and track messages live.
Webhook Management System
A platform to receive, queue, and forward webhook events reliably. The GraphQL backend ingests events, buffers them through RabbitMQ, applies rate limiting, and persists them in PostgreSQL, while the Next.js client lets users inspect payloads and manage endpoints.
IoT Monitoring System
A real-time IoT platform for monitoring and controlling connected devices. Devices stream telemetry over MQTT to a NestJS GraphQL API backed by PostgreSQL and Redis, and the Next.js dashboard visualizes live metrics with charts and exportable reports.
Publications
Face Mask Wearing Recognition System for Big Data Video Streaming
Van-Phuc Le, Thuy-Ngoc Bui, Thanh-Truc Nguyen, Cao-Tien Do, Trong-Hop Do, Arooj Masood, Sungrae Cho
IEEE International Conference on ICT Convergence (ICTC), 2022 · pp. 1101–1106
A real-time computer-vision system that detects whether people in a video stream are wearing face masks. Faces are located with an OpenCV SSD detector, then classified as masked or unmasked by machine-learning models trained on Apache SystemML (running on Spark), with predictions overlaid on a live video feed.
My contribution: First author — researched the approach and implemented the system, including the SystemML-based training pipeline (SVM and logistic-regression classifiers) and the real-time video-streaming inference loop.