Hi, I'm Chihab

Machine Learning Engineer | AI & Backend Developer

Results-driven Machine Learning Engineer with MSc in Software Engineering and BSc in Artificial Intelligence. Delivering end-to-end ML solutions from ideation and prototyping to production deployment.

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Portrait of Chihabeddine Zitouni

About Me

I'm a results-driven Machine Learning Engineer with MSc in Software Engineering and BSc in Artificial Intelligence. Skilled in Python, TensorFlow, and scikit-learn, I specialize in delivering end-to-end ML solutions from ideation and prototyping to production deployment.

My expertise spans computer vision, NLP, and classical ML methods including SVM, decision trees, and XGBoost. I bring strong backend engineering capabilities with APIs, Docker, CI/CD, and clean architecture. I'm adept at working with clients and cross-functional teams to scope, design, and implement scalable AI systems.

6+

Projects Completed

3+

Degrees Earned

15+

Technologies

Experience

June 2025 - Present

AI/Backend Engineer

Mindioo – Prague, Czech Republic

Architecting and developing scalable backend features while integrating cutting-edge AI models into production systems to enhance core product functionality.

  • Architected scalable backend features using TypeScript and Supabase with clean architecture patterns
  • Engineered seamless integrations with Google Calendar and Google Meet APIs
  • Integrated AI models (GPT, Gemini) into production systems
  • Designed and tested robust API endpoints managing full lifecycle development
  • Elevated backend performance through code reviews and best practices implementation
2022 - Present

Bachelor of Science: Artificial Intelligence

Poznan University of Technology, Poland

Pioneering 7-semester AI program in Poland, providing in-depth knowledge of computer sciences and mathematics for practical AI applications. Curriculum covers hot areas of AI research taught by world-recognized scientists in English.

  • Core AI foundations: Machine Learning, Deep Learning, Computer Vision, NLP
  • Advanced topics: Reinforcement Learning, Big Data, Robotics, IoT
  • Specialized areas: Evolutionary Computation, Multi-Agent Systems, Decision Analysis
  • Practical skills: Data Mining, Software Engineering, Cybersecurity, Ethics in AI
  • Research preparation: Scientific methodology, Bachelor's capstone project
2020 - 2022

Master of Science: Software Engineering

Abdelhamid Ibn Badis University, Algeria

Advanced 2-year program focusing on cutting-edge software engineering methodologies, enterprise systems architecture, and emerging technologies with emphasis on research and practical application.

  • Advanced development: Programming Techniques, Service-Oriented Architecture, Model-Driven Engineering
  • Enterprise systems: Information Systems Security, Administration, Web Semantics & Ontology
  • AI & data science: Machine Learning, Data Mining, Multi-Agent Systems, Data Analysis
  • Modern technologies: Cloud & Virtualization, Emerging SE Technologies, Numerical Broadcasting
  • Management & research: Project Management, Operational Research, Scientific Writing, Thesis Defense
2017 - 2020

Bachelor of Science: Computer Science

Abdelhamid Ibn Badis University, Algeria

Comprehensive 3-year program covering theoretical foundations and practical applications in computer science, from mathematical foundations to advanced software systems development.

  • Mathematical foundations: Analysis, Algebra, Probability, Graph Theory, Linear Programming
  • Programming paradigms: Algorithms, Data Structures, OOP, Logic Programming
  • Systems engineering: Computer Architecture, Operating Systems, Databases, Networks
  • Software development: Software Engineering, Web & Mobile Applications, Human-Machine Interface
  • Security & theory: Cryptography, IT Security, Mathematical Logic, Language Theory

Featured Projects

Emzio – Ecommerce Application

Java-based ecommerce app with Firebase Auth and Realtime Database, featuring recommendation system, simplified search, and personalized decision assistance.

Java Firebase ML Recommendations

Multi-Agent System Based on Dynamic Load Balancing

Decentralized intrusion detection using mobile agents and machine learning classifiers (decision tree, SVM, neural networks) with mobile app for network administrators.

Multi-Agent Systems Machine Learning Android Security

SprintLeague – Online Tournament System

Android app with Firebase backend for user management and real-time tournament updates, featuring tournament ladders, conflict resolution, and data validation.

Android Firebase Cloud Functions

Computer Vision Game Board Analysis System

Python/OpenCV system for real-time tracking of game pieces and automated movement notifications with advanced computer vision techniques.

Python OpenCV Computer Vision Real-time Processing

Concrete Crack Detection using CNN and XGBoost

AI-based system for automatic crack detection in concrete images with custom visualization tools, combining deep learning and gradient boosting techniques.

CNN XGBoost Computer Vision TensorFlow

Sentiment Analysis with BERT – NLP Movie Review Classifier

BERT-based sentiment classifier on IMDB dataset using HuggingFace and PyTorch, focused on code quality, evaluation metrics, and reproducibility.

BERT NLP PyTorch HuggingFace

Publications

Conference Paper 2023

A Multi-Agent System Based on Dynamic Load Balancing for Collaborative Intrusion Detection

Hocine, N., & Zitouni, C.

ICNAS 2023, Algiers, Algeria · IEEE

Abstract

Intrusion detection systems contribute to the improvement of the security of systems as well as the confidentiality and integrity of data. These systems are based on traffic analysis, which makes their performance and response time sensitive to the density of network traffic and distributed denial of service attacks. In this paper, we suggest a new collaborative intrusion detection system based on a multi-agent system. The intrusion detection technique relies on dynamic load balancing of traffic analysis to enhance the performance of intrusion detection, especially distributed denial of service attacks. In particular, mobile agents collaborate to analyze traffic using decision trees, support vector machines, and neural networks. The results of the experiment, using NSL-KDD public dataset, show that the proposed intrusion detection system architecture improved the performance and the scalability of intrusion detection compared with state of the art techniques.

Multi-Agent Systems Intrusion Detection Machine Learning Cybersecurity
Repository DOI

Skills & Technologies

Programming Languages

Python Java TypeScript C++ C R

Web & Backend Development

Node.js REST APIs Supabase Firebase Docker CI/CD

Databases

PostgreSQL MySQL MongoDB Firebase Database Supabase

AI & Machine Learning

TensorFlow Keras Scikit-learn OpenCV Decision Trees SVM Neural Networks CNNs XGBoost BERT PyTorch HuggingFace

Data Analysis & Visualization

pandas NumPy Matplotlib Plotly Weka API

Mobile Development

Android Android Studio Firebase Auth Firebase Cloud Functions

Development Tools & IDEs

Git GitHub IntelliJ IDEA Visual Studio Code Maven Gradle Postman Bruno

Software Engineering Practices

Agile (Scrum, Kanban) Clean Architecture TDD Controller-Service-Repository Code Reviews

Languages

Arabic (Native) English (C1) French (B1) Polish (Beginner)

Get In Touch

Let's work together!

I'm always interested in new opportunities and exciting projects. Whether you have a question or just want to say hi, feel free to reach out!

Email

cezitouni@gmail.com

Phone

+48 786 665 255

LinkedIn

chihabeddine-zitouni-a55a29181

GitHub

chihab4real