# Abdalrahman Ibrahim — Complete Content Index # https://geekgineer.com/llms-full.txt # Generated at build time. Updated with every deployment. # For a short curated link index, see: https://geekgineer.com/llms.txt --- ## IDENTITY Name: Abdalrahman Ibrahim Role: Head of AI Products Development Employer: AGILOX, Linz, Austria (2022–present) Domain: Private LLM infrastructure, agentic platforms, autonomous mobile robots, on-device ML GitHub: https://github.com/geekgineer LinkedIn: https://www.linkedin.com/in/abdalrahman-m-amer/ ORCID: https://orcid.org/0009-0005-8184-230X Email: hi@geekgineer.com --- ## ABOUT At AGILOX I lead AI products development. The core platform is a private LLM microservices stack serving 250+ employees — paired with an internal AI gateway that issues virtual API keys for developer and agentic-coding workflows, with routing, fallbacks, and per-team cost dashboards. In November 2025 the gateway handled 12,970 requests and 27.3M tokens at 93.5% success. Commercial API spend dropped ~80% per month, with data staying entirely on-premise. On top of the platform, I run hybrid Graph+Vector RAG for technical documentation and connect LLMs to live ROS 2 telemetry through VAgent using the Model Context Protocol (MCP) for natural-language fleet diagnostics. The inference layer uses vLLM with AWQ quantization on private GPU clusters. I graduated first of class from Misr University for Science and Technology with a B.Sc. in Mechatronics Engineering (CGPA 3.96/4.0, 2018), then spent a year at Infineon Technologies researching Graph Neural Networks for automated analog circuit recognition — improving accuracy 15% and cutting compute time 30% on small-data constraints. An M.Sc. in Autonomous Systems and Robotics at the University of Klagenfurt followed in 2022. I joined AGILOX as Senior Research Engineer, became AI Lead Engineer in May 2023, and was promoted to Head of AI Products Development in January 2026. In parallel I maintain open-source C++ inference tooling built around one constraint: it must drop in without dragging a framework. YOLOs-CPP is the largest — a header-only library running YOLO v5 through v12 on ONNX Runtime and OpenCV, covering detection, segmentation, oriented bounding boxes, and pose estimation, with no Python in the inference path. The strategy-pattern design means switching task types or YOLO versions is a one-line change. The same philosophy carries through ros2_yolos_cpp, Depths-CPP, and the rest of the ecosystem: production-ready inference engines, minimal dependency footprint. --- ## TECHNICAL EXPERTISE Languages: C++, Python, TypeScript LLM Infrastructure: vLLM, LiteLLM, AWQ quantization, MCP, RAG (Graph+Vector), Runpod Robotics: ROS 2, ONNX Runtime, OpenCV, LiDAR, sensor fusion, SLAM ML: PyTorch, ONNX, Graph Neural Networks, computer vision, edge AI Tools: Docker, Git, Linux, GitHub Actions --- ## PROJECTS Total: 10 (10 featured) ### YOLOs-CPP URL: https://geekgineer.com/work/yolos-cpp Markdown: https://geekgineer.com/work/yolos-cpp.md Year: 2025 Status: active Role: Creator & Maintainer Featured: true Tagline: Header-only C++ library for real-time YOLO inference — detection, segmentation, pose, OBB — no Python, no runtime bloat Stack: C++17, ONNX Runtime, OpenCV, CUDA, YOLOv5–v12, Quantization (INT8/FP16) Tags: C++, Computer Vision, ONNX Runtime, Object Detection, Edge AI, Robotics, Real-Time Inference GitHub: https://github.com/Geekgineer/YOLOs-CPP Demo: https://www.youtube.com/watch?v=Ax5vaYJ-mVQ ### YOLOs-CPP-TensorRT URL: https://geekgineer.com/work/yolos-cpp-rt Markdown: https://geekgineer.com/work/yolos-cpp-rt.md Year: 2026 Status: active Role: Creator & Maintainer Featured: true Tagline: Header-only C++ YOLO library for NVIDIA TensorRT — GPU preprocessing, CUDA Graph replay, sub-2ms latency, 530+ FPS Stack: C++17, TensorRT ≥10, CUDA ≥12, OpenCV, CUDA Graph Tags: C++, TensorRT, Computer Vision, CUDA, Object Detection, Edge AI, Real-Time Inference GitHub: https://github.com/Geekgineer/YOLOs-CPP-TensorRT ### ros2_yolos_cpp URL: https://geekgineer.com/work/ros2-yolos-cpp Markdown: https://geekgineer.com/work/ros2-yolos-cpp.md Year: 2024 Status: active Role: Creator & Maintainer Featured: true Tagline: Production-grade ROS2 nodes for YOLO inference — detection, segmentation, pose, OBB, classification with lifecycle management Stack: C++17, ROS2 (Humble / Jazzy), ONNX Runtime, OpenCV, vision_msgs, sensor_msgs Tags: C++, ROS2, Computer Vision, Robotics, Object Detection, Real-Time Inference GitHub: https://github.com/Geekgineer/ros2_yolos_cpp ### motcpp URL: https://geekgineer.com/work/motcpp Markdown: https://geekgineer.com/work/motcpp.md Year: 2024 Status: active Role: Creator & Maintainer Featured: true Tagline: 10 SOTA multi-object trackers in modern C++17 — 10–100× faster than Python, unified API, ONNX ReID backend Stack: C++17, ONNX Runtime, OpenCV, Eigen3, ByteTrack, BoostTrack, GoogleTest Tags: C++, Multi-Object Tracking, Computer Vision, ONNX Runtime, Robotics, Real-Time Inference, Edge AI GitHub: https://github.com/Geekgineer/motcpp ### SmolVLM2-ROS2 URL: https://geekgineer.com/work/smolvlm2-ros2 Markdown: https://geekgineer.com/work/smolvlm2-ros2.md Year: 2025 Status: active Role: Creator & Maintainer Featured: true Tagline: On-device Vision-Language Model for robotics — SmolVLM2 running via ONNX Runtime inside a ROS2 node for scene understanding and spatial reasoning Stack: C++17, Python, ROS2, ONNX Runtime, SmolVLM2, Transformers Tags: VLM, ROS2, On-device ML, ONNX Runtime, Robotics, Computer Vision, Generative AI GitHub: https://github.com/Geekgineer/SmolVLM2-ROS2 ### Depths-CPP URL: https://geekgineer.com/work/depths-cpp Markdown: https://geekgineer.com/work/depths-cpp.md Year: 2024 Status: active Role: Creator & Maintainer Featured: true Tagline: Header-only C++ monocular depth estimation — Depth Anything v2 via ONNX Runtime, real-time on CPU and GPU Stack: C++17, ONNX Runtime, OpenCV, Depth Anything v2, CUDA Tags: C++, Depth Estimation, Computer Vision, ONNX Runtime, Edge AI, Monocular Depth GitHub: https://github.com/Geekgineer/Depths-CPP ### CloudPeek URL: https://geekgineer.com/work/cloudpeek Markdown: https://geekgineer.com/work/cloudpeek.md Year: 2024 Status: maintained Role: Creator & Maintainer Featured: true Tagline: Single-header C++ point cloud viewer — OpenGL 3.3 rendering, arcball camera, no PCL or Open3D required Stack: C++17, OpenGL 3.3, GLFW, GLEW Tags: C++, Point Cloud, LiDAR, 3D Vision, Robotics, Visualisation GitHub: https://github.com/Geekgineer/CloudPeek ### dynamic_lidar_interpolation URL: https://geekgineer.com/work/dynamic-lidar-interpolation Markdown: https://geekgineer.com/work/dynamic-lidar-interpolation.md Year: 2024 Status: maintained Role: Creator & Maintainer Featured: true Tagline: ROS2 C++ package for real-time LiDAR point cloud interpolation — five methods from nearest-neighbour to spline, tested on Velodyne and Ouster Stack: C++17, ROS2, PCL, Eigen3, Point Cloud Processing Tags: C++, ROS2, LiDAR, Point Cloud, Sensor Fusion, Robotics GitHub: https://github.com/Geekgineer/dynamic_lidar_interpolation ### ros2_bag_exporter URL: https://geekgineer.com/work/ros2-bag-exporter Markdown: https://geekgineer.com/work/ros2-bag-exporter.md Year: 2024 Status: maintained Role: Creator & Maintainer Featured: true Tagline: C++ ROS2 package that exports bag files to images, PCD, IMU, GPS, and CSV — YAML-configured, sqlite3 and MCAP support Stack: C++17, ROS2, rosbag2, OpenCV, PCL, YAML-CPP Tags: C++, ROS2, Data Engineering, Dataset Creation, Robotics GitHub: https://github.com/Geekgineer/ros2_bag_exporter ### kokoro-onnx-cpp URL: https://geekgineer.com/work/kokoro-onnx-cpp Markdown: https://geekgineer.com/work/kokoro-onnx-cpp.md Year: 2025 Status: active Role: Creator & Maintainer Featured: true Tagline: On-device text-to-speech in C++ — Kokoro TTS running via ONNX Runtime for low-latency voice synthesis on embedded and robot hardware Stack: C++17, ONNX Runtime, Kokoro TTS, HiFi-GAN, Audio Processing Tags: C++, TTS, ONNX Runtime, On-device ML, Audio, Edge AI, Robotics GitHub: https://github.com/Geekgineer/kokoro-onnx-cpp --- ## PUBLICATIONS Total: 6 peer-reviewed ### [1] FlexiNet: An Adaptive Feature Synthesis Network for Real-Time Ego Vehicle Speed Estimation Authors: Abdalrahman Ibrahim, Kyandoghere Kyamakya, Wolfgang Pointner Venue: IEEE Access Year: 2025 Type: journal DOI: 10.1109/ACCESS.2025.3562229 DOI URL: https://doi.org/10.1109/ACCESS.2025.3562229 Keywords: Computer Vision, Autonomous Vehicles, Deep Learning, Speed Estimation Abstract: This paper introduces FlexiNet, a novel adaptive feature synthesis network designed for real-time ego vehicle speed estimation in autonomous driving systems. FlexiNet dynamically synthesizes features across multiple scales and modalities, achieving state-of-the-art accuracy while maintaining the throughput required for real-time deployment on vehicle hardware. ### [2] A Graph Attention Network Based System for Robust Analog Circuits' Structure Recognition Involving a Novel Data Augmentation Technique Authors: Ali Deeb, Mohamed Salem, Abdalrahman Ibrahim, Joachim Pichler, Sergii Tkachov, Karaj Anjeza, Fadi Al Machot, Kyandoghere Kyamakya Venue: IEEE Access Year: 2024 Type: journal DOI: 10.1109/ACCESS.2024.3367598 DOI URL: https://doi.org/10.1109/ACCESS.2024.3367598 Keywords: Graph Neural Networks, Circuit Analysis, Pattern Recognition, Data Augmentation Abstract: This work presents a comprehensive system using Graph Attention Networks for robust recognition of analog circuit structures. A novel data augmentation technique is introduced to improve generalization across diverse schematic layouts. The system achieves high recognition accuracy on real-world IP analog circuits, enabling automation of a previously manual verification task. ### [3] A Comprehensive Generalization of a Graph-Attention-Network GAT Based System Towards Real IP Analog-Mixed-Signal AMS Schematics Structure Recognition Authors: Ali Deeb, Mohamed Salem, Abdalrahman Ibrahim, Witesyavwirwa Vianney Kambale, Joachim Pichler, Fadi Al Machot, Kyandoghere Kyamakya Venue: 30th IEEE International Conference on Electronics, Circuits and Systems (ICECS) Year: 2023 Type: conference DOI: 10.1109/ICECS58634.2023.10382859 DOI URL: https://doi.org/10.1109/ICECS58634.2023.10382859 Keywords: Graph Attention Networks, Circuit Recognition, Mixed-Signal Systems Abstract: A comprehensive generalization approach for GAT-based systems applied to real IP analog-mixed-signal (AMS) schematic structure recognition. The work demonstrates that graph attention architectures generalize across schematic styles and technology nodes without retraining from scratch. ### [4] A Vertical Systematic Generalization Towards Real IP Analog-Mixed-Signal AMS Schematics Structure Recognition Authors: Ali Deeb, Mohamed Salem, Abdalrahman Ibrahim, Witesyavwirwa Vianney Kambale, Joachim Pichler, Fadi Al Machot, Kyandoghere Kyamakya Venue: 27th International Conference on Circuits, Systems, Communications and Computers (CSCC) Year: 2023 Type: conference DOI: 10.1109/CSCC58962.2023.00053 DOI URL: https://doi.org/10.1109/CSCC58962.2023.00053 Keywords: Systematic Generalization, AMS Circuits, Pattern Recognition Abstract: A systematic approach to generalizing analog-mixed-signal schematic recognition systems across vertical abstraction levels — from device-level schematics to block-level topologies. The method enables recognition at multiple levels of hierarchy without separate training pipelines. ### [5] Study of Massive Floating Solar Panels over Lake Nasser Authors: Mohamed A. Tolba, Abdalrahman Ibrahim, Ahmed M. El-Nahas, Basem Elboshy, Bassem M. Ramzy, Ahmed M. El-Garhy Venue: International Journal of Photoenergy Year: 2021 Type: journal DOI: 10.1155/2021/6674091 DOI URL: https://doi.org/10.1155/2021/6674091 PDF: https://www.hindawi.com/journals/ijp/2021/6674091/ Keywords: Renewable Energy, Floating Solar, Environmental Impact, Sustainability Abstract: Comprehensive study on the technical feasibility, energy output modeling, and environmental impact of deploying massive floating solar panel arrays over Lake Nasser, Egypt. The analysis covers thermal performance, water evaporation reduction, and grid integration potential. ### [6] Evaluation of Capacitive Deionization Desalination Technology for Irrigation Authors: Abdalrahman Ibrahim, O.A. Abdullatef, H.F. Abd-Elhamid, A.M. El-Nahas, M.A. Tolba, Bassem M. Ramzy Venue: Desalination and Water Treatment Year: 2020 Type: journal DOI: 10.5004/dwt.2020.25676 DOI URL: https://doi.org/10.5004/dwt.2020.25676 Keywords: Desalination, Capacitive Deionization, Irrigation, Water Treatment Abstract: Evaluation of capacitive deionization (CDI) technology as a sustainable, energy-efficient solution for irrigation water desalination in arid regions. The study benchmarks CDI performance against conventional reverse osmosis and assesses viability for small-scale agricultural applications. --- ## EDUCATION Ph.D. Candidate, Smart Systems Alpen-Adria-Universität Klagenfurt Focus: Agentic AI for Robotics/Transportation In progress, part-time M.Sc. Autonomous Systems & Robotics Alpen-Adria-Universität Klagenfurt · 2022 B.Sc. Mechatronics Engineering Misr University for Science and Technology · 2018 First of class · CGPA 3.96/4.0 · Honor Degree --- ## CONTACT Email: hi@geekgineer.com GitHub: https://github.com/geekgineer LinkedIn: https://www.linkedin.com/in/abdalrahman-m-amer/ ORCID: https://orcid.org/0009-0005-8184-230X --- ## SITE STRUCTURE Home: https://geekgineer.com/ Home (Markdown): https://geekgineer.com/index.md Work index: https://geekgineer.com/work Work (Markdown): https://geekgineer.com/work.md Now: https://geekgineer.com/now Uses: https://geekgineer.com/uses Work — YOLOs-CPP: HTML: https://geekgineer.com/work/yolos-cpp Markdown: https://geekgineer.com/work/yolos-cpp.md Work — YOLOs-CPP-TensorRT: HTML: https://geekgineer.com/work/yolos-cpp-rt Markdown: https://geekgineer.com/work/yolos-cpp-rt.md Work — ros2_yolos_cpp: HTML: https://geekgineer.com/work/ros2-yolos-cpp Markdown: https://geekgineer.com/work/ros2-yolos-cpp.md Work — motcpp: HTML: https://geekgineer.com/work/motcpp Markdown: https://geekgineer.com/work/motcpp.md Work — SmolVLM2-ROS2: HTML: https://geekgineer.com/work/smolvlm2-ros2 Markdown: https://geekgineer.com/work/smolvlm2-ros2.md Work — Depths-CPP: HTML: https://geekgineer.com/work/depths-cpp Markdown: https://geekgineer.com/work/depths-cpp.md Work — CloudPeek: HTML: https://geekgineer.com/work/cloudpeek Markdown: https://geekgineer.com/work/cloudpeek.md Work — dynamic_lidar_interpolation: HTML: https://geekgineer.com/work/dynamic-lidar-interpolation Markdown: https://geekgineer.com/work/dynamic-lidar-interpolation.md Work — ros2_bag_exporter: HTML: https://geekgineer.com/work/ros2-bag-exporter Markdown: https://geekgineer.com/work/ros2-bag-exporter.md Work — kokoro-onnx-cpp: HTML: https://geekgineer.com/work/kokoro-onnx-cpp Markdown: https://geekgineer.com/work/kokoro-onnx-cpp.md LLM index: https://geekgineer.com/llms.txt Full dump: https://geekgineer.com/llms-full.txt Sitemap: https://geekgineer.com/sitemap-index.xml Humans: https://geekgineer.com/humans.txt