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Al Amin Hossain Nahid

Full-Stack Developer

I build scalable web and mobile applications — from front-end interfaces to back-end systems and Android apps.

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About

Building with purpose,
learning by doing.

I'm a computer science student and early-career full-stack developer who learns best by building projects. I enjoy creating complete web applications by combining front-end technologies with back-end services and databases.

Through academic and personal projects, I'm developing a strong foundation in software development — currently interning at AuraDev LTD, contributing to real product work.

B.Tech — Computer Science & Engineering, AIUB

Languages

  • C++
  • Java
  • C#C#
  • Kotlin
  • Python
  • TypeScript
  • JavaScript

Frontend

  • React
  • Next.js
  • Tailwind CSS
  • Bootstrap
  • HTML
  • CSS

Backend & DB

  • NestJS
  • FastAPI
  • ASP .NET
  • PostgreSQL
  • MySQL
  • MongoDB
  • TypeORM
  • Entity Framework
  • Prisma

Tools

  • Git
  • Docker
  • Postman
  • Firebase
  • Vercel
  • Android Studio

Services

What I Build

  • 01

    Full-Stack Web Development

    Building complete web applications using modern frontend and backend technologies with databases and REST APIs — from design to deployment.

  • 02

    Android App Development

    Developing native Android applications using Kotlin and modern tooling to create efficient, user-friendly mobile experiences that connect to real backends.

Experience

My Journey

June 2026 – Present

Software Engineering Intern

AuraDev LTD
Intern

Contributing to GrantOS as a Software Engineering Intern at AuraDev — involved in end-to-end product development, from backend integration to building client-facing features within a professional, agile engineering environment.

Product DevelopmentBackend IntegrationFrontend Integration

Work

Selected Projects

A collection of projects showcasing my expertise in full-stack web and mobile development.

View all repositories →

Research

“Intelligent Waste Classification for Sustainable Urban Development”

PECCII 2026 · Jhenaidah, Bangladesh

A MobileNetV3-based deep learning approach using the BDWaste dataset, focused on classifying waste images for smarter and more sustainable urban waste-management workflows.

Co-authored with Ferdus Hossain, Tariful Islam Fahim, and Salman Zzoha. Accepted at the International Conference on Power, Electronics, Communications, Computing, and Intelligent Infrastructure.

Highlights

MobileNetV3BDWaste DatasetDeep LearningUrban Sustainability
Status
Accepted
Year
2026
View Conference

Contact

Let's build something
together.

robin.nahid123@gmail.com →

Or send a message

© 2026 Al Amin Hossain Nahid
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