Rajab İskenderli

• Developed AI-powered software components for government-sector projects, with a primary focus on Vision Language Models (VLMs).
• Designed and implemented multimodal AI pipelines capable of understanding and processing both visual and textual information.
• Built production-ready AI modules for document understanding, information extraction, and workflow automation.
• Integrated large vision-lan

• Led the AI teaching team, coordinating instructors and maintaining the quality and consistency of educational content.
• Designed and continuously improved the AI curriculum, developing structured syllabi covering machine learning, deep learning, computer vision, natural language processing, and generative AI.
• Created lectures, presentations, programming assignments, hands-on projects, and ass

• Conducted research at the intersection of deep learning and Fourier Transform Ion Cyclotron Resonance (FT-ICR) mass spectrometry to improve spectral resolution using AI.
• Designed and implemented PeakPrecision, a hybrid neural architecture combining multi-scale CNNs, BiLSTMs, and Transformer attention for frequency-domain super-resolution.
• Developed OmniLoss, a composite loss function tailore

• Developed deep learning solutions for satellite and aerial imagery analysis, focusing on object detection, semantic segmentation, and instance segmentation.
• Fine-tuned and optimized state-of-the-art computer vision models including YOLO, SAM 2, and Mask R-CNN for domain-specific geospatial datasets.
• Built end-to-end computer vision pipelines for processing orthophotos, satellite imagery, and

• Developed AI-powered conversational assistants for Azerbaijan’s public sector, including an intelligent chatbot for the MyGov digital government platform and a specialized AI assistant for the Ministry of Ecology and Natural Resources.
• Designed and implemented Retrieval-Augmented Generation (RAG) pipelines using vector databases, embedding models, and advanced retrieval strategies to enable ac

• Developed an AI-powered conversational assistant for the French-Azerbaijani University (UFAZ) to provide students and staff with intelligent access to university information and resources.
• Designed and implemented a Retrieval-Augmented Generation (RAG) pipeline using LangChain, vector databases, and embedding models to enable accurate, context-aware responses based on UFAZ-specific documents.