Farrukh Olimov

Built and deployed end-to-end AI pipelines for large language models (LLMs), vision-language models (VLMs), and computer vision systems, supporting multimodal generative applications. Implemented distributed inference architecture using Ray Serve and Ray Clusters, enabling scalable, low-latency model serving across multiple nodes.

Developed graph neural network (GNN) models for molecular property prediction, supporting scientific research and accelerating decision-making in drug discovery. Refactored legacy codebases for improved performance, modularity, and integration with new ML pipelines, ensuring long-term maintainability.

Led the development of a scalable system for analyzing and detecting hazardous website content using large language models. Enhanced GitLab CI/CD pipelines, improving deployment reliability, test automation, and integration speed.

Conducted applied research on a range of NLP tasks, including image captioning, machine translation, and text summarization, contributing to state-of-the-art model development and evaluation. Investigated core computer vision problems such as object detection and action recognition, focusing on improving model accuracy and optimizing real-time inference performance.

Grade: 3.94 / 4.5

Pursued a Master of Science degree focusing on advanced topics in electrical engineering and computer science. Gained in-depth knowledge in areas relevant to modern technological advancements.

Grade: 4.23 / 4.5

Completed a Bachelor of Science degree in Electrical Engineering and Computer Science, building a strong foundation in core engineering principles and computational methods. Developed skills essential for analyzing and solving complex technical problems.

Grade: 4.23 / 4.5

Undertook a Bachelor of Arts degree in Business Administration, acquiring knowledge in management, economics, and organizational behavior. Developed analytical and strategic thinking skills applicable to various business contexts.