Hi, my name is Faig Gafarov. I am a Computer Vision & AI Engineer and a graduate of Mechatronics Engineering from Yildiz Technical University.
I have hands-on experience in developing real-time industrial quality control systems and deploying deep learning models in production environments. I am skilled in Python, OpenCV, and YOLO-based detection pipelines, with additional expertise in data science and PLC integration. With a strong engineering background and practical AI experience, I am motivated to apply innovative computer vision and artificial intelligence technologies to solve complex real-world and industrial challenges.
BSc in Mechatronics Engineering (English) | 2020 - 2025
Aygaz | July 2025 - October 2025
• Built a real-time computer vision QC system with CNNs & YOLOv11 for torn/intact/faded sleeve detection.
• Developed a multi-camera Python/OpenCV pipeline with ROI optimization and real-time inference.
• Integrated with Omron PLCs (FINS/EtherNet/IP) to enable automated rejection and seamless production line control.
• Implemented valve angle detection with a smart vision camera, optimizing lighting and imaging for reliable inspection.
C4IR Azerbaijan | April 2025 - July 2025
• Developed automated time-series forecasting solutions and data visualization tools, using machine learning models.
• Implemented end-to-end pipelines for data processing, model evaluation, and PDF reporting with customized visual analytics.
DroneX UAV | February 2022 - August 2022
• GUI Backend Development, contributed to the design interface and development of unmanned aerial vehicles.
• Contributed to a real-time image processing system that classified targets by color (enemy vs. ally units) and triggered corresponding autonomous actions for mission scenarios.
• Achieved 3rd place in Teknofest 2022 UAV Competition and selected as a finalist in Teknofest Azerbaijan with the same team.
I design and train AI-driven computer vision models that learn, reason, and make decisions based on visual data. My work focuses on building intelligent models for object detection, visual understanding, and real-time inference.
I develop AI systems that act as intelligent agents by combining perception, decision-making, and automated actions. These systems integrate vision models with logic, control systems, and real-time constraints to operate autonomously.
I deploy and optimize AI models for real-world environments, ensuring fast inference, robustness, and scalability. My experience includes model optimization, real-time deployment, and integration with industrial hardware and production systems.
Developed a real-time computer vision system for cylinder sleeve inspection using CNNs and YOLOv11, integrated with Omron PLCs to enable automated quality control on the production line. The system ensures high-accuracy defect detection and seamless integration with industrial hardware for continuous monitoring.
Developed an AI-based image processing system to automate the separation of fresh and rotten fruits on a conveyor belt using Python and OpenCV. Applied pre-trained CNN models with color and texture analysis to ensure real-time, high-accuracy classification for industrial quality control applications.
An AI-powered video analysis application that automatically extracts slides from presentations and generates intelligent summaries from YouTube videos using Google's Gemini 2.0 Flash model. The system combines computer vision and natural language processing to provide comprehensive video content analysis.
Developed an angle detection system using Hikrobot smart cameras to identify cylinder valve orientations for automated cap installation. The system performs precise angle measurements to ensure proper alignment and automated assembly in industrial production environments.
Developed a Smart Home System using Flutter Dark Dream for the interface, integrated with PIC hardware. Implemented Pultus protocol for communication using Assembly and C, and added Voice Recognition features for enhanced user control and seamless home automation.
Contributed to the GUI backend development and design interface for unmanned aerial vehicles. Developed a real-time image processing system that classified targets by color to distinguish enemy and ally units, triggering corresponding autonomous actions for mission scenarios.
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