Worked on expanding KerasHub’s capabilities by integrating multilingual dense retrieval and open-source embedding model support, focusing on cross-language semantic search and embedding workflows. Developed new model classes, preprocessors, and conversion utilities to add XLM-RoBERTa-based dense retrieval and support for Harrier-OSS text embedding models, including Gemma3 and Qwen3 architectures. Enhanced code quality and stability through formatting, test alignment, and error handling improvements. Leveraged Python, Keras, and Transformers to enable seamless onboarding of new models and more reliable production performance. These contributions to the keras-team/keras-hub repository improved developer productivity and broadened language coverage for semantic search applications.