Mahmoud Najmeh
Java
Python
C#
JavaScript
SQL
Spring Boot

Mahmoud Najmeh

JAVA BACKEND DEVELOPER FULL-STACK ENGINEER AI ENTHUSIAST
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About Me

Dedicated software developer with expertise in OOP, web, and AI development. Proficient in Java, Spring Boot, Python, Django, and C# .NET, with a proven track record in building robust backend and full-stack applications.

Education

  • Java Backend Developer DCI Digital Career Institute, Berlin (06/2023 – 08/2024)
  • IT Specialist for Application Development Combard GmbH, Berlin (05/2020 – 05/2022)

Technical Skills

Languages

Frameworks

Tools & Platforms

Featured Projects

Missing Person Alert System

Missing Person Alert System FullStack

Police tool developed with Spring Boot, SwiftUI & Jetpack Compose featuring Firebase push notifications, geolocation tracking, and admin panel.

Spring Boot SwiftUI Jetpack Compose Firebase Integration Tests
6 Months View Project
Banking System

Banking System FullStack

Fullstack banking application with email/IP tracking, multi-currency support, transaction processing, and comprehensive testing with Mockito.

Java Spring Boot Mockito React Integration Tests
6 Months View Project
AI Text Summarizer

AI Text Summarizer FullStack

Multilingual text summarization application powered by Spring Boot REST API with advanced NLP capabilities.

Spring Boot REST API AI NLP
2 Months View Project
Student Portal

Student Portal FullStack

Comprehensive educational platform featuring role-based access control, grading system, PDF export functionality, and online testing.

Django Role-based Auth PDF Export UI Validation
4 Months View Project
AI Face Analysis

AI Face Analysis

Image classification system built with Django and TensorFlow, featuring automated processing and custom GUI interface.

Django TensorFlow Python Computer Vision
4 Months View Project
Medical Diagnosis System

Medical Diagnosis System

A machine learning-based desktop application built with .NET and ML.NET that predicts potential diseases from user-provided symptoms. Features include natural language symptom input, disease prediction with confidence score, and a WPF-based user-friendly interface.

.NET ML.NET WPF Machine Learning
2 Months View Project

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