عن
Abdalrahman Ibrahim
متاح للاستشارات والتوجيه والفرص بدوام كامل.
في AGILOX، أقود تطوير منتجات الذكاء الاصطناعي. أبني بنية تحتية خاصة لنماذج اللغة الكبيرة وأنظمة ذكاء اصطناعي على متن الروبوتات للروبوتات المتنقلة ذاتية الحركة، مع الاحتفاظ بجميع البيانات داخل المنشأة.
تخرجت الأول على دفعتي من جامعة مصر للعلوم والتكنولوجيا بدرجة بكالوريوس في الهندسة الميكاترونية (CGPA 3.96/4.0، 2018)، ثم حصلت على درجة الماجستير في الأنظمة الذاتية والروبوتات من جامعة كلاغنفورت في 2022. انضممت إلى AGILOX كمهندس بحث أول، ثم مهندس رئيسي للذكاء الاصطناعي، وترقيت إلى رئيس تطوير منتجات الذكاء الاصطناعي في يناير 2026.
أحافظ بالتوازي على أدوات استنتاج C++ مفتوحة المصدر مبنية حول قيد واحد: يجب أن تُضمَّن دون سحب إطار عمل كامل. YOLOs-CPP هي الأكبر — مكتبة header-only تشغّل YOLO من v5 إلى v12 على ONNX Runtime وOpenCV. نفس الفلسفة تمتد عبر ros2_yolos_cpp وDepths-CPP وبقية النظام البيئي.
أنا طالب دكتوراه بدوام جزئي في جامعة كلاغنفورت، متخصص في الذكاء الاصطناعي الوكيل للروبوتات والنقل. متاح للاستشارات والتوجيه والفرص بدوام كامل.
السيرة المهنية
Head of AI Products Development
AGILOX · Linz, Austria
- Leading AI products and platform across the company. Scope spans private LLM infrastructure, agentic platforms, and on-robot AI.
AI Lead Engineer
AGILOX · Linz, Austria
- 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.
Senior Research Engineer
AGILOX · Linz, Austria
- Production LiDAR + Camera perception stacks.
- Edge-hardware model deployment; simulation data cut physical sensor testing time by ~40%.
Researcher
Infineon Technologies · Villach, Austria
- Graph Neural Networks for automated analog circuit recognition; +15% accuracy via small-data augmentation, -30% compute time.
Robotics & AI Engineer
Zewail City / iRobotX · Egypt
- ROS-based social robots and deep learning pipelines for medical CT imaging.
- Evaluated Visual SLAM and reinforcement-learning control for humanoid robotics.
التعليم
Ph.D. Candidate, Smart Systems
Alpen-Adria-Universität Klagenfurt
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
منشورات مختارة
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.
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.
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.
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.
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.
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.