Jasmin Bogatinovski
Senior Deep Learning Engineer @Signatrix
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WORK HISTORY
Senior Deep Learning Engineer @Signatrix
Berlin, DE
Owned core components of a real-time multimodal vision AI platform for retail loss prevention, spanning video ingestion, data pipelines, model training, and production serving across multiple stores.• Designed and maintained scalable ML pipelines for video processing, annotation, training, and inference, supporting extreme object detection (~30k object classes), classification, and ranking models in production environments.• Led development of object tracking systems in highly dynamic settings, reducing tracking failures by 20%+ and directly improving theft detection accuracy.• Architected and adapted deep learning models for robust tracking under heavy occlusion and rapid motion, translating research ideas into production-ready architectures deployed in real-time systems.
EDUCATION
University of "SS. Cyril and Methodius", Skopje
Bachelor's degree, Computer System Engeenering Automation and Robotics
Technische Universität Berlin
Doctor of Philosophy - PhD, Machine Learning
Jozef Stefan International Postgraduete School
Master's degree, Artificial Intelligence
Gymnasium Kole Nehtenin
Electrotechnitian for computer technology and automation, Computer technology and automation
ABOUT JASMIN BOGATINOVSKI
Senior AI Systems Engineer (Applied ML) with 7+ years of experience designing and owning end-to-end production ML systems, specializing in foundation models, computer vision, and large-scale data pipelines. Currently leading real-time AI systems for retail loss prevention, deploying multimodal models across production environments and delivering measurable business impact. Previously led applied research initiatives at Technical University of Berlin in collaboration with Huawei, building NLP-driven failure prediction systems that improved cloud reliability and resulted in patent and high-impact publications. Holds a PhD in AI with deep expertise in deep learning, generative AI, and scalable ML infrastructure. Known for translating research-grade models into reliable production systems.
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