Mohamed MostafaMetawea
Specialized in Computer Vision, Deep Learning & Generative AI
About Me
Passionate AI Engineer building intelligent solutions
Junior AI and Communication & Electronics Engineering graduate with strong hands-on experience in Computer Vision, Deep Learning, and Generative AI, including object detection, segmentation, face recognition, and diffusion-based image generation.
Proficient in Python, data preprocessing, feature engineering, model optimization, and evaluation, with experience deploying ML models as REST APIs using FastAPI, Docker, and cloud platforms.
AI/ML
Deep Learning
Computer Vision
Advanced
Gen AI
Diffusion/LLMs
Deployment
FastAPI/Docker
3.44/4.0
GPA
A
Project Grade
31+
GitHub Repos
98%
Anti-Spoofing
Technical Skills
Expertise in cutting-edge AI technologies
Soft Skills
Experience
Professional journey in AI and ML
Giza, Egypt
- Developed and optimized ML models for industrial use cases, improving model performance and reliability
- Deployed trained models as RESTful APIs using Flask, enabling real-time inference
- Containerized ML services using Docker, ensuring reproducible and scalable deployment
Projects
Real-world AI solutions I've built
- Built intelligent multi-agent system for hospital workflows
- Integrated Generative AI for medical assistance
- Real-time patient data processing and analysis
AI
Agents
Multi
Modal
GenAI
Powered
- Reduced manual attendance by 80%
- 98% spoof prevention accuracy
- 95% overall accuracy with sub-second inference
80%
Time Saved
98%
Anti-Spoof
<1s
Inference
- Complete ML pipeline for clinical risk prediction
- 92% recall and 0.90 F1-score achieved
- Interactive web application deployed
92%
Recall
0.90
F1-Score
A+
Grade
- Automated resume screening and job matching
- NLP-based similarity scoring
- Reduced recruitment time significantly
NLP
Based
Smart
Match
Auto
Screen
- Implemented GAN architecture for image generation
- Trained on fashion dataset
- High-quality image synthesis
GAN
Based
Deep
Learning
HD
Output
- CNN-based audio feature extraction
- Multi-class audio classification
- High accuracy sound recognition
CNN
Model
Multi
Class
High
Accuracy
- Image-based disease detection
- CNN model for classification
- Agricultural AI application
CNN
Based
Plant
Health
Auto
Detect
- TF-IDF based text classification
- Improved content moderation accuracy
- Reduced false positives through feature engineering
NLP
Based
Low
False +
High
Accuracy
- Biometric iris detection
- Deep learning feature extraction
- High accuracy recognition
Bio
Metric
Deep
Learn
High
Acc
- Real-time emotion detection
- CNN-based feature extraction
- Multi-class emotion classification
CNN
Deep
Real
Time
7+
Emotions
- Patient risk prediction models
- Healthcare data analytics
- Clinical decision support
ML
Based
Health
Care
Smart
Predict
- Production-ready API development
- ML model serving endpoints
- Docker containerization
REST
API
Fast
API
Docker
Ready
Education
Academic foundation in engineering and AI
Giza, Egypt
3.44/4.0
GPA
A
Project Grade
5 Years
Duration
Graduation Project
AI-Based Face Recognition Attendance System with Anti-Spoofing
Built a real-time face recognition system with liveness detection, achieving 95% accuracy and 98% spoof prevention.
Courses & Training
Continuous learning and professional development
Generative AI Professional
DEPI, Ministry of Communications and IT
Nov 2025 - Jun 2026AI Training
National Telecommunication Institute (NTI)
Oct 2025 - Jan 2026Machine Learning Specialization
DeepLearning.AI (Andrew Ng)
CompletedIBM Data Science Program
DEPI, Ministry of Communications and IT
Oct 2024 - Jun 2025Orange AI Level 1
Orange
Nov 2024Get In Touch
Let's discuss opportunities and collaborations
Contact Information
Languages
Arabic
Native
English
Very Good