S A Saharukh

Led the development of Munchies, a comprehensive food tech platform including 3 mobile apps (User, Rider, Restaurant), a user website, and an admin panel. Built with Flutter for mobile apps, React and Next.js for frontend, and Node.js (Express and NestJS) for backend, featuring 4 microservices. Orchestrated system architecture, team collaboration, and efficient implementation to scale for 70k+ users and 300+ restaurants.Frontend: Flutter, React, Next.js, Backend: Node.js (Express, NestJS), Microservices Architecture, Scalable Infrastructure Design, Team Leadership and Collaboration

Developed a VPN app using Flutter for both Android and iOS platforms. Leveraged Android's and iOS's native VPN APIs to provide secure internet connections on mobile devices. Designed an intuitive user interface and implemented features for seamless VPN usage.Frontend: Flutter (Dart), Native VPN APIs (Android, iOS), Secure Connection Protocols, Intuitive User Interface

Created Examiner exam app with Flutter and Firebase, offering a user-friendly interface and personalized tests. Received positive feedback, enhancing education access and learning outcomes in Bangladesh. Proficient in Flutter and Firebase integration, showcasing strong design and problem-solving skills.Frontend: Flutter (Dart), Backend: Firebase, Push Notification

As the lead developer for ChatOut, I orchestrated a major overhaul, improving UI, integrating social media via API, and optimizing communication. Played a key role in database design and backend development, using Flutter, Dart, NestJS, TypeScript, and Node.js for an enhanced user experience and operational efficiency.Frontend: Flutter (Dart), Backend: NestJS (TypeScript, Node.js), Database: MongoDB, Social Media Integration: API Integration, Realtime web socket, Push Notification

Built for Cookspace , this a food ordering production mobile app(Android & iOS) . includes API, Payment Gateway, realtime web socket, Push Notification

This was made for a client whos form Doha Qatar

Trained a convolutional neural network to classify between ~ 80 cats breeds. Over 60,000 cats were classified before server bills made the project too expensive to continue hosting.