HELLO THERE!
I'm Dhieddine BARHOUMI
-Machine Learning Engineer
-MLOps Enthusiast
ABOUT ME
I'm a computer science engineering student at INSAT university, specializing in Artificial Intelligence and passionate about its incredible quick progress.
👨💻 ML & MLOps Enthusiast
📜
Google Cloud Professional ML Engineer
Certified.
📜
IBM AI Engineer
Certified.
📜
Microsoft Azure AI
Certified.
☁️ Experienced in Google Cloud Platform (GCP) and Microsoft Azure Platform.
WHAT I OFFER
As a machine learning engineer with cloud expertise, I specialize in building end-to-end ML solutions from development to deployment. I combine MLOps practices with cloud-native technologies to deliver scalable, production-ready AI systems.
SKILLS
With a knack for quick learning, I focus on mastering many skills and technologies needed for Machine Learning.
Python
Advanced
SQL
Advanced
C++
Proficient
Java
Proficient
R
Proficient
TensorFlow
Advanced
PyTorch
Advanced
Scikit-learn
Advanced
LangChain
Proficient
Hugging Face
Proficient
SpaCy
Advanced
NLTK
Advanced
Pandas
Advanced
NumPy
Advanced
OpenCV
Proficient
Git
Advanced
Docker
Advanced
Kubernetes
Advanced
Airflow
Proficient
MLflow
Proficient
Google Cloud
Advanced
Azure
Advanced
Vertex AI
Advanced
BigQuery
Advanced
Looker
Proficient
EXPERIENCE
Through diverse internships and hands-on roles, I've developed practical skills
and contributed to innovative solutions.
- Selected as a DAAD KOSPIE Scholar for advanced AI research in autonomous driving.
- Developed sensor fusion models integrating LiDAR and cameras for enhanced perception.
- Implemented transformer-based deep learning to improve scene understanding.
- Simulated AI-driven perception in CARLA, refining models before real-world testing.
- Optimized multi-modal pipelines for better AI performance across diverse driving conditions.
- Trained and fine-tuned AI models using high-performance GPUs for real-time processing.
- Upon completion of this six-month internship, I will graduate.
- Developed AI Security Systems for historic sites (e.g., Ajloun Castle, Amman Citadel), integrating real-time threat detection and automated alerts.
- Enhanced ANPR Accuracy by 20%, optimizing recognition for high-speed and low-light conditions on major streets in Amman.
- Integrated AI Models with Milestone VMS for intelligent object detection and motion tracking, automating alerts and cutting false alarms by 30%.
- Tested AI Surveillance Solutions for diverse environments, refining system adaptability.
- Managed Multiple Projects in Parallel, balancing resources and meeting strict deadlines.
- Adapted to International Work Environment, gaining experience in cross-cultural collaboration and project execution.
- Conducted research on multiple computer vision algorithms, choosing YOLOv8 for optimal speed and accuracy.
- Fine-tuned YOLOv8 for detecting angle and gusset objects in boxes, achieving 92.5% precision and 60% mAP50-95.
- Deployed the model as a real-time API on Microsoft Azure, integrating it with a dashboard for defect detection.
- Automated quality control, triggering alerts for misaligned or missing objects as boxes moved on a conveyor.
- Collaborated with a multidisciplinary team to replace manual defect detection for an industry client, boosting efficiency by 30%.
- Completed Microsoft Azure AI Fundamentals training and certification during the internship.
PROJECTS
Dive into a showcase of projects where I applied my technical expertise
to solve real-world challenges and create impactful solutions.
Personal Project
- Developed a scalable machine learning application for real-time loan approval decisions.
- Designed and deployed an end-to-end pipeline including data ingestion, transformation, and model training with hyperparameter tuning and grid search.
- Implemented custom utilities, logging, and exception handling for efficient debugging and streamlined operations.
- Built a Flask-based backend with a responsive frontend using HTML and Bootstrap for user interaction.
- Automated CI/CD processes with GitHub Actions, ensuring seamless integration and deployment to Azure Web App Service.
- Dockerized the application and hosted it on Azure Container Registry for scalable cloud deployment.
Academic Project
- Designed a reinforcement learning-based system to navigate complex indoor environments.
- Simulated realistic home-like environments in Gazebo with dynamic obstacles.
- Leveraged ROS2 for seamless integration, utilizing RViz for real-time monitoring of the robot's path and sensor data.
- Collaborated with colleagues, employing GitHub for CI/CD, ensuring robust version control and seamless updates.
- Implemented the TD3 algorithm with a custom reward function to optimize path planning and obstacle avoidance.