Javid Guliyev

Implemented LLM-based multi-agent architectures and real-time AI integrations serving millions of customers; applied prompt engineering and tool integration to virtual assistant chatbots while contributing to backend AI solutions.

Led an AI engineering team delivering scalable computer vision and remote sensing ML solutions for aerial imagery, optimizing models for instance segmentation and land classification and maintaining AI infrastructure for deployment and monitoring.

Developed and delivered undergraduate computer science lectures and supervised students, providing mentorship and academic guidance.

Developed deep learning models (CNNs, GANs, super-resolution), built Airflow data pipelines, implemented OCR and RAG solutions using LangChain/OpenAI, and deployed Streamlit apps while managing PostgreSQL/MSSQL databases.

Taught Python, machine learning, and deep learning through hands-on sessions, guided students on projects using TensorFlow, PyTorch, and Scikit-Learn, and provided career mentorship.

Evaluated ML algorithms for magnetic-field-based positioning, built high-performance models (RF, XGBoost, SVM) and optimized hyperparameters with Optuna/Hyperopt while conducting EDA and 3D visualizations.

Developed API endpoints with ASP.NET/C#, implemented database operations, and built responsive web pages to enhance analytical visualization for Inloya.com and partner projects.

Completed a Master of Science in Data Science and Artificial Intelligence with coursework and projects focused on machine learning and AI applications.

Completed a Software Engineering program focused on practical software development skills and technologies.

Earned a Bachelor of Science in Computer Engineering covering software development, algorithms, and systems engineering.