Über 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.
Arabisch
Muttersprachlich oder zweisprachig
Deutsch
Konversationssicher
Englisch
Verhandlungssicher
Vor Ort möglich
Dortmund (bis zu 50 km), München (bis zu 50 km), Berlin (bis zu 50 km), Hamburg (bis zu 50 km)
Projekt- und Berufserfahrung
- FH DORTMUNDUniversity research assistantAUTOMOBILSEKTORMärz 2023 - April 2025 (2 Jahre)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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Ausbildung und Abschlüsse
- 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
Zertifizierungen
- CourseraIntroduction to Self-Driving Cars2021
- CourseraState Estimation and Localization for Self-Driving Cars2021