MAHMOUDMUHAMMAD
Turning research-driven AI into scalable, real-world products.
About
I'm a Junior AI Engineer and Data Scientist with a B.Sc. in Artificial Intelligence (Honors). My focus spans LLM-powered applications, RAG architectures, predictive analytics, and computer vision. I thrive at the intersection of deep learning research and practical software engineering — building systems that don't just work in notebooks, but scale in the real world.
Military Status: Exempted ✓{
"name": "Mahmoud Muhammad",
"role": "Junior AI Engineer & Data Scientist",
"location": "Cairo, Egypt",
"status": "Open to Opportunities",
"languages": ["Arabic", "English"],
"education": "B.Sc. Artificial Intelligence (Honors)",
"interests": ["Generative AI", "RAG Systems", "Computer Vision"]
}Technical Arsenal
Featured Projects
SpendHiST
AI-powered receipt management & spending analytics
Multi-agent backend with OCR pipeline supporting English & Arabic receipts. Features RAG-based chat over spending data, JWT auth, and intelligent analytics dashboard.
NexaOS
AI-Powered Desktop Environment
Full-featured desktop environment with intelligent document editors, custom file formats (.nd, .np, .ndf), and real-time collaboration via Socket.io.
Interactive Multi-PDF Chat
RAG pipeline for semantic PDF search
Conversational AI system using FAISS + TinyLlama/Phi-2 + Sentence-Transformers for semantic search over multiple PDF documents. Deployed on Streamlit Cloud.
AI Churn Prediction Platform
Graduation Project — Telecom customer churn prediction
Stacking ensemble model (XGBoost + LightGBM + RF) achieving 91% CV accuracy and 84.5% ROC-AUC. Full-stack platform with multi-tenant REST backend and JWT auth.
Land Cover Classification
ResNet50-based satellite image classification (EuroSAT)
Deep learning app for real-time EuroSAT land-cover classification across 10 categories with confidence scoring, visual analytics, and a modular Streamlit interface integrating TensorFlow, OpenCV, and Plotly.
Energy Consumption Forecasting
Time-series ML pipeline for household electricity prediction
End-to-end ML pipeline forecasting hourly electricity usage on the UCI dataset (2M+ records). Engineered lag features & time-based attributes. XGBoost achieved R²=0.58, MAE=0.32; Prophet enabled interpretable seasonal forecasting.
Experience
Beetleware
AI Engineer Intern(Remote)
Developed and fine-tuned AI/ML models for production SaaS products. Built end-to-end data pipelines, integrated LLM-powered features into existing platforms, and deployed full-stack solutions with modern APIs and CI/CD. Collaborated in Agile teams, contributing to open-source AI tooling.
Digital Egypt Pioneers Initiative (DEPI)
Data Science Trainee(Hybrid)
Built and evaluated machine learning models for classification and regression tasks using Python, Pandas, scikit-learn, and TensorFlow. Worked on industry-simulation projects covering end-to-end data science workflows — from data cleaning and EDA to model selection and evaluation. Mentored peers in core ML and data analysis concepts.
Education
Egyptian Russian University (ERU)
Bachelor of Artificial Intelligence
Grade: Very Good (Honors)
Graduation Project
AI-Powered Churn Prediction Platform for Telecom Companies
Key Courses
Certifications
IBM AI & Data Science
DEPI / MCIT
Deep Learning with PyTorch
Mahara Tech (ITI)
Machine Learning Specialization
Coursera / DeepLearning.AI & Stanford
Certified Python Data Associate
DataCamp
Developing Applications with LangChain
DataCamp
Multi-Agent Systems with LangGraph
DataCamp
AWS Cloud Practitioner Essentials
Amazon Web Services
ML & DL Training
Zewail City
Research & Publications
Customer Churn in Telecom: Predictive Modeling for Enhanced Retention Strategies
Egyptian Russian University
Stacking ensemble approach combining XGBoost, LightGBM, and Random Forest with a Django-React full-stack analytics platform.
Read PaperA Study of Generative Approaches for Balancing Imbalanced Data: SMOTE, GANs, and LLMs
Published on ResearchGate
Comparative study of SMOTE, GANs (CTGAN/TVAE), and LLMs for fraud detection, concluding traditional sampling offers superior stability over generative models.
Read PaperContact
Let's Build
Something Great
I'm always open to discussing new projects, creative ideas, or opportunities to be part of something amazing.