Moustafa Mohamed
AI Developer | Specializing in Machine Learning, Deep Learning & LLM Engineering
AI Developer | Specializing in Machine Learning, Deep Learning & LLM Engineering
I'm a second-year Software Engineering student specializing in Artificial Intelligence and Data Science. My expertise spans machine learning, deep learning, and large language models, with a passion for building intelligent systems that solve real-world problems.
My technical journey began with C/C++, establishing strong foundations in algorithms and system-level programming. I've since mastered Python for AI/ML development, along with frameworks like TensorFlow, PyTorch, and Keras. My recent focus has been on LLM engineering, generative AI, and developing production-ready data science solutions.
Beyond coding, I'm passionate about the entire data pipeline - from ETL processes to deploying machine learning models. I've developed expertise in building end-to-end AI systems, including my published Python library for streamlined data cleaning and EDA.
Deep Learning
Neural Networks
Generative AI
NLP
LLM Engineering
Prompt Engineering
LLaMA
Pipelines
RAG
Fine-Tuning (LoRA/QLoRA)
AI Agents
Function Calling
Code Optimization
Multi-modal AI
Data Analysis
Data Cleaning
Data Visualization
Machine Learning
EDA
ETL
Plotly
Power BI
Python
PyTorch
TensorFlow
Keras
C/C++
JavaScript
SQL
Git
GitHub
PyPI
API Dev
OOP
HuggingFace
LangChain
Gradio
Model Deployment
Advanced web content extraction and summarization tool using Gemini AI and LLaMA 3.2. Processes any URL to generate concise markdown summaries with key points extraction.
PyTorch-based feedforward neural network for tumor classification. Achieves high accuracy in predicting malignant vs benign cases.
CNN model classifying 36 different fruits/vegetables with 92% accuracy. Implemented image augmentation and transfer learning.
NLP system classifying SMS messages as spam/ham with multiple ML models. Includes text preprocessing, TF-IDF vectorization, and deployed Streamlit interface.
Unsupervised learning project using K-Means clustering to identify distinct customer groups based on spending patterns.
Comprehensive data analysis identifying survival patterns. Feature engineering and visualization techniques uncovered key demographic insights.
Analyzed salary distributions across San Francisco city employees. Created interactive visualizations to showcase pay disparities and trends.
A curated collection of practical ML projects covering classification, regression, and fraud detection. Includes complete implementations from data preprocessing to model deployment.
Linear Regression model with Streamlit interface for used car valuation
XGBoost model achieving 99.96% accuracy on imbalanced data
Comparative analysis of multiple classifiers on medical data
Logistic Regression for rock vs mine classification
I'm actively seeking internship opportunities and collaborations in AI/ML and Data Science. Whether you have a project idea or just want to connect, feel free to reach out!