During February 2026, Jeejeelee integrated ColBERT late interaction model support into the jeejeelee/vllm repository, focusing on enhancing document retrieval and ranking capabilities. Leveraging Python and expertise in machine learning and natural language processing, Jeejeelee implemented per-token embeddings and MaxSim scoring to improve both the accuracy and efficiency of document scoring across large corpora. The work included comprehensive documentation, example scripts, and robust tests to ensure reliable production adoption and clear usage patterns. By prioritizing code quality and validation coverage, Jeejeelee enabled more effective document ranking workflows without introducing new bugs, demonstrating depth in both engineering and domain knowledge.