Based in Montreal, Quebec, I build intelligent systems that balance theory, engineering rigor, and deployment realism. My work centers on privacy-preserving machine learning, modular distributed architectures, and optimization for hardware- and network-constrained environments.
Software Engineer & AI Systems Builder
Building scalable AI and distributed software systems that solve real business and operational challenges.
I am Mouhamad Arafeh, a software engineer focused on scalable systems, AI-powered applications, and reliable backend architecture. I build solutions that connect strong engineering foundations with practical delivery, performance, and long-term maintainability.
About Me
Research-driven engineer specializing in federated learning, distributed systems, and scalable AI architectures.
I bring experience across academia, applied research, and industry, with contributions spanning federated learning, blockchain-based security, large-scale data systems, and full stack enterprise development.
Skills & Expertise
Technical strengths across AI, distributed infrastructure, security, and software engineering.
AI & Machine Learning
Advanced work in federated learning, reinforcement learning integration, non-IID data handling, and privacy-preserving ML systems.
- Federated Learning
- Reinforcement Learning
- TensorFlow
- PyTorch
Distributed Systems
Design of modular and scalable learning infrastructures for multi-client, resource-aware, and containerized environments.
- Docker
- Kubernetes
- Parallel Clients
- Network Simulation
Security, IoT & Engineering
Applied engineering across blockchain security, IoT optimization, and full stack product delivery for real operational systems.
- Hyperledger Fabric
- IoT Optimization
- Python
- React / Java / C# / PHP / Kotlin
Research
Publications and themes focused on modular, scalable, and privacy-preserving machine learning.
Elsevier • 2023
Information Sciences
Published work on data-independent warmup strategies for federated learning under non-IID conditions, improving learning behavior in decentralized settings.
Elsevier • 2023
Internet of Things
Introduced ModularFed, a modular framework for federated learning designed to improve experimentation, reuse, and architectural clarity.
IEEE • 2024
IEEE Internet of Things Journal
Advanced warmup-based federated sequential learning methods tailored for realistic IoT and distributed training environments.
Selected Work
Professional and research-driven systems spanning AI, security, analytics, and enterprise development.
Research Infrastructure
Federated Learning Frameworks
Designed modular distributed learning frameworks using Docker and Kubernetes, addressing client bias, non-IID data, scalability, and resource constraints.
Security & Blockchain
Secure Mobile Crowdsensing Architecture
Built a blockchain-based security model with Hyperledger Fabric to support decentralized trust and fake sensing detection in mobile crowdsensing systems.
Industry Systems
Enterprise Tracking Solutions
Delivered NFC attendance systems, GPS employee monitoring, RFID asset tracking, and secure offline-capable enterprise applications in a full stack role.
Experience
A career path connecting research, advanced systems engineering, and real-world product delivery.
Research Associate • ÉTS • 2025 – Present
Advancing AI techniques for cybersecurity, distributed learning in IoT, reinforcement learning within federated learning, and graduate student mentorship.
PhD Researcher • ÉTS • 2020 – 2025
Designed a novel federated learning framework, built containerized distributed environments, and published research in IEEE and Elsevier venues.
Full Stack Developer • Tragging • 2015 – 2018
Built enterprise-grade software products including NFC attendance systems, GPS employee monitoring, RFID asset tracking, offline-capable applications, and secure access control solutions using Python, Java, C#, PHP, Kotlin, and React.
Research Assistant • University of Milan & University of Wollongong in Dubai • 2018 – 2019
Contributed to large-scale social network analysis, recommendation systems, blockchain-based security, and distributed data processing across applied research environments.
Contact
Open to research, advanced engineering, and AI systems opportunities.
Montreal, Quebec, Canada
arafeh198@gmail.com
(514) 629-1119
GitHub
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LinkedIn