Developed video-enabled multimodal visual-language embedding for the kvcache-ai/sglang repository, expanding the model’s capabilities to process and combine text, images, and video inputs. Leveraging Python and expertise in backend and API development, the work involved updating the warm-up and initialization sequence to robustly handle new input types during server startup. This technical enhancement improved deployment reliability and enabled new use cases such as video-aware search and multimodal content understanding. The approach focused on extending machine learning infrastructure to support richer data modalities, thereby increasing the flexibility and business value of the platform for advanced content analysis and retrieval scenarios.