Software Engineer | Academic Lecturer | Machine Learning & MLOps Practitioner
- 🎓 Education: B.Sc. in Engineering & Information Technology (2024).
- 💼 Academic Experience: Faculty Member / Academic Lecturer at Al-Ittihad University, Al-Razi University, and Ibn Sirin College, teaching courses across Artificial Intelligence, Information Technology, and Information Systems.
- 🔬 Technical Profile: Software Engineer specializing in end-to-end Machine Learning deployment, MLOps automation, and custom web/desktop application development.
- ⚡ Core Focus: Building MLOps CI/CD pipelines, Computer Vision applications with YOLOv8, Retrieval-Augmented Generation (RAG) chatbots, and containerized REST APIs.
| Category | Skills & Technologies |
|---|---|
| Programming Languages | Python, C#, JavaScript, SQL, PHP, Dart (Flutter), HTML/CSS |
| AI & Data Science | YOLOv8 (Ultralytics), OpenCV, LangChain, ChromaDB, Groq API, Hugging Face, Scikit-Learn |
| MLOps & Cloud/DevOps | Docker, GitHub Actions (CI/CD), Pytest, Flake8, Cloud Development |
| Frameworks & Web | FastAPI, Streamlit, .NET (C#), Flutter |
| Database Management | SQL Server, PostgreSQL, MySQL, Oracle Database XE |
| Methodologies & Tools | Agile/Scrum, Git/GitHub, Docker Desktop, Adobe Suite (After Effects, Photoshop, Illustrator) |
| Languages | Arabic (Native), English (Advanced), Chinese (Good) |
- Configured end-to-end CI/CD workflows via GitHub Actions with automated testing (
pytest) and linting (flake8). - Integrated real-time object tracking and detection using YOLOv8 and Streamlit packed inside a Docker container.
2. 🤖 RAG PDF Chatbot App
- PDF document Q&A application utilizing LangChain, ChromaDB, and Hugging Face Embeddings.
- High-speed LLM inference powered by the Groq API served through a Streamlit dashboard.
- ML model trained with Random Forest and packaged as a FastAPI REST service.
- Containerized using Docker for seamless deployment and paired with a Streamlit interface.
- Text analysis dashboard for sentiment classification and linguistic metric extraction using Python NLP tools.
"Combining academic instruction with hands-on engineering to build intelligent, scalable software systems."