{
  "$schema": "https://raw.githubusercontent.com/jsonresume/resume-schema/v1.0.0/schema.json",
  "basics": {
    "name": "Abdalrahman Ibrahim",
    "label": "Head of AI Products Development",
    "email": "hi@geekgineer.com",
    "url": "https://geekgineer.com",
    "summary": "Head of AI Products Development at AGILOX. Builds private LLM infrastructure and on-robot AI for autonomous mobile robots. PhD candidate at the University of Klagenfurt on Agentic AI for Robotics and Transportation.",
    "location": {
      "city": "Linz",
      "countryCode": "AT",
      "region": "Austria"
    },
    "profiles": [
      {
        "network": "GitHub",
        "username": "geekgineer",
        "url": "https://github.com/geekgineer"
      },
      {
        "network": "LinkedIn",
        "username": "abdalrahman-m-amer",
        "url": "https://www.linkedin.com/in/abdalrahman-m-amer/"
      },
      {
        "network": "ORCID",
        "username": "0009-0005-8184-230X",
        "url": "https://orcid.org/0009-0005-8184-230X"
      }
    ]
  },
  "work": [
    {
      "name": "AGILOX",
      "position": "Head of AI Products Development",
      "location": "Linz, Austria",
      "startDate": "2026-01-01",
      "highlights": [
        "Leading AI products and platform across the company. Scope spans private LLM infrastructure, agentic platforms, and on-robot AI."
      ]
    },
    {
      "name": "AGILOX",
      "position": "AI Lead Engineer",
      "location": "Linz, Austria",
      "startDate": "2023-05-01",
      "endDate": "2026-01-01",
      "highlights": [
        "Architected a private LLM microservices platform serving 250+ employees.",
        "Migrated commercial APIs to private-hosted models — ~80% monthly LLM cost reduction with full data privacy.",
        "Built an internal AI gateway issuing virtual API keys for developer and agentic-coding workflows; routing, fallbacks, usage controls, per-team cost dashboards. 7-day window in Nov 2025: 12,970 requests, 27.3M tokens, 93.5% success.",
        "Custom agentic orchestration layer for intent-aware routing and sub-200ms token streaming.",
        "Hybrid Graph + Vector RAG for technical documentation, with evaluation-driven tuning loops.",
        "vLLM + AWQ quantization for high-throughput 70B+ model serving on private GPU clusters (Runpod).",
        "VAgent: integrated LLMs with ROS 2 telemetry via the Model Context Protocol (MCP) for natural-language fleet diagnostics."
      ]
    },
    {
      "name": "AGILOX",
      "position": "Senior Research Engineer",
      "location": "Linz, Austria",
      "startDate": "2022-10-01",
      "endDate": "2023-05-01",
      "highlights": [
        "Production LiDAR + Camera perception stacks.",
        "Edge-hardware model deployment; simulation data cut physical sensor testing time by ~40%."
      ]
    },
    {
      "name": "Infineon Technologies",
      "position": "Researcher",
      "location": "Villach, Austria",
      "startDate": "2021-10-01",
      "endDate": "2022-10-01",
      "highlights": [
        "Graph Neural Networks for automated analog circuit recognition; +15% accuracy via small-data augmentation, -30% compute time."
      ]
    },
    {
      "name": "Zewail City / iRobotX",
      "position": "Robotics & AI Engineer",
      "location": "Egypt",
      "startDate": "2018-01-01",
      "endDate": "2021-01-01",
      "highlights": [
        "ROS-based social robots and deep learning pipelines for medical CT imaging.",
        "Evaluated Visual SLAM and reinforcement-learning control for humanoid robotics."
      ]
    }
  ],
  "education": [
    {
      "institution": "Alpen-Adria-Universität Klagenfurt",
      "studyType": "Ph.D. Candidate, Smart Systems",
      "courses": [
        "In progress, part-time"
      ]
    },
    {
      "institution": "Alpen-Adria-Universität Klagenfurt",
      "studyType": "M.Sc. Autonomous Systems & Robotics",
      "endDate": "2022-01-01"
    },
    {
      "institution": "Misr University for Science and Technology",
      "studyType": "B.Sc. Mechatronics Engineering",
      "endDate": "2018-01-01",
      "courses": [
        "First of class · CGPA 3.96/4.0 · Honor Degree"
      ]
    }
  ],
  "publications": [
    {
      "name": "FlexiNet: An Adaptive Feature Synthesis Network for Real-Time Ego Vehicle Speed Estimation",
      "publisher": "IEEE Access",
      "releaseDate": "2025-01-01",
      "url": "https://doi.org/10.1109/ACCESS.2025.3562229",
      "summary": "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.\n"
    },
    {
      "name": "A Graph Attention Network Based System for Robust Analog Circuits' Structure Recognition Involving a Novel Data Augmentation Technique",
      "publisher": "IEEE Access",
      "releaseDate": "2024-01-01",
      "url": "https://doi.org/10.1109/ACCESS.2024.3367598",
      "summary": "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.\n"
    },
    {
      "name": "A Comprehensive Generalization of a Graph-Attention-Network GAT Based System Towards Real IP Analog-Mixed-Signal AMS Schematics Structure Recognition",
      "publisher": "30th IEEE International Conference on Electronics, Circuits and Systems (ICECS)",
      "releaseDate": "2023-01-01",
      "url": "https://doi.org/10.1109/ICECS58634.2023.10382859",
      "summary": "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.\n"
    },
    {
      "name": "A Vertical Systematic Generalization Towards Real IP Analog-Mixed-Signal AMS Schematics Structure Recognition",
      "publisher": "27th International Conference on Circuits, Systems, Communications and Computers (CSCC)",
      "releaseDate": "2023-01-01",
      "url": "https://doi.org/10.1109/CSCC58962.2023.00053",
      "summary": "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.\n"
    },
    {
      "name": "Study of Massive Floating Solar Panels over Lake Nasser",
      "publisher": "International Journal of Photoenergy",
      "releaseDate": "2021-01-01",
      "url": "https://doi.org/10.1155/2021/6674091",
      "summary": "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.\n"
    },
    {
      "name": "Evaluation of Capacitive Deionization Desalination Technology for Irrigation",
      "publisher": "Desalination and Water Treatment",
      "releaseDate": "2020-01-01",
      "url": "https://doi.org/10.5004/dwt.2020.25676",
      "summary": "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.\n"
    }
  ],
  "skills": [
    {
      "name": "LLM Infrastructure",
      "keywords": [
        "vLLM",
        "LiteLLM",
        "AWQ",
        "Runpod",
        "MCP",
        "RAG",
        "Vector DB",
        "Graph DB"
      ]
    },
    {
      "name": "Robotics & Perception",
      "keywords": [
        "ROS 2",
        "ONNX Runtime",
        "OpenCV",
        "LiDAR",
        "Sensor Fusion",
        "SLAM"
      ]
    },
    {
      "name": "Languages",
      "keywords": [
        "C++17",
        "Python",
        "TypeScript"
      ]
    },
    {
      "name": "ML & AI",
      "keywords": [
        "PyTorch",
        "ONNX",
        "Graph Neural Networks",
        "Computer Vision",
        "Edge AI"
      ]
    }
  ],
  "languages": [
    {
      "language": "Arabic",
      "fluency": "native"
    },
    {
      "language": "English",
      "fluency": "fluent"
    },
    {
      "language": "German",
      "fluency": "learning"
    }
  ],
  "projects": [
    {
      "name": "YOLOs-CPP",
      "description": "Header-only C++ library for real-time YOLO inference — detection, segmentation, pose, OBB — no Python, no runtime bloat",
      "highlights": [
        "Role: Creator & Maintainer",
        "Status: active"
      ],
      "keywords": [
        "C++",
        "Computer Vision",
        "ONNX Runtime",
        "Object Detection",
        "Edge AI",
        "Robotics",
        "Real-Time Inference"
      ],
      "startDate": "2025-01-01",
      "url": "https://github.com/Geekgineer/YOLOs-CPP",
      "roles": [
        "Creator & Maintainer"
      ],
      "type": "application"
    },
    {
      "name": "YOLOs-CPP-TensorRT",
      "description": "Header-only C++ YOLO library for NVIDIA TensorRT — GPU preprocessing, CUDA Graph replay, sub-2ms latency, 530+ FPS",
      "highlights": [
        "Role: Creator & Maintainer",
        "Status: active"
      ],
      "keywords": [
        "C++",
        "TensorRT",
        "Computer Vision",
        "CUDA",
        "Object Detection",
        "Edge AI",
        "Real-Time Inference"
      ],
      "startDate": "2026-01-01",
      "url": "https://github.com/Geekgineer/YOLOs-CPP-TensorRT",
      "roles": [
        "Creator & Maintainer"
      ],
      "type": "application"
    },
    {
      "name": "ros2_yolos_cpp",
      "description": "Production-grade ROS2 nodes for YOLO inference — detection, segmentation, pose, OBB, classification with lifecycle management",
      "highlights": [
        "Role: Creator & Maintainer",
        "Status: active"
      ],
      "keywords": [
        "C++",
        "ROS2",
        "Computer Vision",
        "Robotics",
        "Object Detection",
        "Real-Time Inference"
      ],
      "startDate": "2024-01-01",
      "url": "https://github.com/Geekgineer/ros2_yolos_cpp",
      "roles": [
        "Creator & Maintainer"
      ],
      "type": "application"
    },
    {
      "name": "motcpp",
      "description": "10 SOTA multi-object trackers in modern C++17 — 10–100× faster than Python, unified API, ONNX ReID backend",
      "highlights": [
        "Role: Creator & Maintainer",
        "Status: active"
      ],
      "keywords": [
        "C++",
        "Multi-Object Tracking",
        "Computer Vision",
        "ONNX Runtime",
        "Robotics",
        "Real-Time Inference",
        "Edge AI"
      ],
      "startDate": "2024-01-01",
      "url": "https://github.com/Geekgineer/motcpp",
      "roles": [
        "Creator & Maintainer"
      ],
      "type": "application"
    },
    {
      "name": "SmolVLM2-ROS2",
      "description": "On-device Vision-Language Model for robotics — SmolVLM2 running via ONNX Runtime inside a ROS2 node for scene understanding and spatial reasoning",
      "highlights": [
        "Role: Creator & Maintainer",
        "Status: active"
      ],
      "keywords": [
        "VLM",
        "ROS2",
        "On-device ML",
        "ONNX Runtime",
        "Robotics",
        "Computer Vision",
        "Generative AI"
      ],
      "startDate": "2025-01-01",
      "url": "https://github.com/Geekgineer/SmolVLM2-ROS2",
      "roles": [
        "Creator & Maintainer"
      ],
      "type": "application"
    },
    {
      "name": "Depths-CPP",
      "description": "Header-only C++ monocular depth estimation — Depth Anything v2 via ONNX Runtime, real-time on CPU and GPU",
      "highlights": [
        "Role: Creator & Maintainer",
        "Status: active"
      ],
      "keywords": [
        "C++",
        "Depth Estimation",
        "Computer Vision",
        "ONNX Runtime",
        "Edge AI",
        "Monocular Depth"
      ],
      "startDate": "2024-01-01",
      "url": "https://github.com/Geekgineer/Depths-CPP",
      "roles": [
        "Creator & Maintainer"
      ],
      "type": "application"
    },
    {
      "name": "CloudPeek",
      "description": "Single-header C++ point cloud viewer — OpenGL 3.3 rendering, arcball camera, no PCL or Open3D required",
      "highlights": [
        "Role: Creator & Maintainer",
        "Status: maintained"
      ],
      "keywords": [
        "C++",
        "Point Cloud",
        "LiDAR",
        "3D Vision",
        "Robotics",
        "Visualisation"
      ],
      "startDate": "2024-01-01",
      "url": "https://github.com/Geekgineer/CloudPeek",
      "roles": [
        "Creator & Maintainer"
      ],
      "type": "application"
    },
    {
      "name": "dynamic_lidar_interpolation",
      "description": "ROS2 C++ package for real-time LiDAR point cloud interpolation — five methods from nearest-neighbour to spline, tested on Velodyne and Ouster",
      "highlights": [
        "Role: Creator & Maintainer",
        "Status: maintained"
      ],
      "keywords": [
        "C++",
        "ROS2",
        "LiDAR",
        "Point Cloud",
        "Sensor Fusion",
        "Robotics"
      ],
      "startDate": "2024-01-01",
      "url": "https://github.com/Geekgineer/dynamic_lidar_interpolation",
      "roles": [
        "Creator & Maintainer"
      ],
      "type": "application"
    },
    {
      "name": "ros2_bag_exporter",
      "description": "C++ ROS2 package that exports bag files to images, PCD, IMU, GPS, and CSV — YAML-configured, sqlite3 and MCAP support",
      "highlights": [
        "Role: Creator & Maintainer",
        "Status: maintained"
      ],
      "keywords": [
        "C++",
        "ROS2",
        "Data Engineering",
        "Dataset Creation",
        "Robotics"
      ],
      "startDate": "2024-01-01",
      "url": "https://github.com/Geekgineer/ros2_bag_exporter",
      "roles": [
        "Creator & Maintainer"
      ],
      "type": "application"
    },
    {
      "name": "kokoro-onnx-cpp",
      "description": "On-device text-to-speech in C++ — Kokoro TTS running via ONNX Runtime for low-latency voice synthesis on embedded and robot hardware",
      "highlights": [
        "Role: Creator & Maintainer",
        "Status: active"
      ],
      "keywords": [
        "C++",
        "TTS",
        "ONNX Runtime",
        "On-device ML",
        "Audio",
        "Edge AI",
        "Robotics"
      ],
      "startDate": "2025-01-01",
      "url": "https://github.com/Geekgineer/kokoro-onnx-cpp",
      "roles": [
        "Creator & Maintainer"
      ],
      "type": "application"
    }
  ]
}