About Rajab
About Me: Embedded AI Engineer with an M.Sc. in Embedded Systems and experience in developing automotive FMCW radar-based perception systems for intelligent infrastructure applications. Background in automotive radar signal processing, object detection, classification, and tracking, with deployment of machine learning models on NVIDIA Jetson platforms for real-time embedded operation. Experienced in system integration, sensor data processing pipelines, and performance optimization for reliable operation under real-world conditions. Familiar with DevOps and CI/CD practices to support automated testing, reproducibility, and maintainable deployment workflows.
Key Skills:
1. Automotive Radar and Perception: FMCW radar signal processing, parameter optimization, micro-Doppler analysis, sensor fusion, radar-based classification (Machine learning, Deep learning), model deployment, ONNX, Pytorch, TensorFlow, scikit-learn, Pandas.
2. Sensors & Data: Radar, LiDAR and camera data acquisition, calibration and labeling.
3. Embedded Systems: AVR microcontrollers, NVIDIA Jetson AGX Orin, real-time systems, embedded C.
4. System Design: System architecture, requirements definition, modular software design, sensor calibration, ROS2.
5. DevOps: Docker, Kubernetes, CI/CD pipelines, Jenkins, GitLab, Linux, AWS, Terraform, Ansible, Grafana.
Kalman filter Tracking: Linear, Extended, Unscented.
Arabic
Native or bilingual
German
Conversational
English
Fluent
Can work on-site
Dortmund (up to 50km), München (up to 50km), Berlin (up to 50km), Hamburg (up to 50km)
Experience
- FH DORTMUNDUniversity research assistantAUTOMOBILEMarch 2023 - April 2025 (2 years)44 Dortmund, GermanyKey Skills:- Radar signal processing: FMCW radar, Range-Doppler Maps, Micro-Doppler signatures- Radar classification: Machine Learning (SVM) and Deep Learning (CNN, TimeDistributed CNN+LSTM)- Radar object tracking- AI Model Deployment: ONNX Runtime, concurrent inference for multiple radars- Real-Time Systems: ROS 2, UDP socket communication- Data Clustering: DBSCAN, HDBSCAN- Academic Autonomous driving projects: Radar/Lidar occupancy grids, Visual Odometry, Sensor Fusion (GPS, IMU, LIDAR with EKF)- Strong experience with Python, Keras, MATLAB, and embedded Linux systems.- Experience with traffic scenario classification at intersections using stationary radar setups- NVIDIA Jetson AGX Orin optimization for real-time inferenceAchievements:- First-author publication: Comparison of Single Frame Classification with Micro-Doppler Classification of VRUs for Traffic Radar (IEEE, 2024)- Second-author publication (accepted but not published yet): RADAR TRACKING ENHANCEMENT UTILIZING TARGET SIZE ESTIMATION BASED ON THE RANGE-DOPPLER MAP- Developing an automated radar data labeling pipeline using sensor fusion with mounted cameras- Deploying an End-to End real-time radar perception modules on embedded platforms (Nvidia Jetson AGX Orin), System communication via ROS2
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Education
- DevOps DiplomaBootcamp2026Jenkins- DevOps- Docker- Linux- Terraform- Ansible- Amazon Web Services (AWS) Grafana- Kubernetes- Gitlab CI/CD
- Master in Embedded systems for mechatronicsFH DORTMUND2022Master in Embedded systems for mechatronics
Certifications
- CourseraIntroduction to Self-Driving Cars2021
- CourseraState Estimation and Localization for Self-Driving Cars2021