Developed Qwen3.5 text model bridges, including both dense and Mixture-of-Experts (MoE) variants, for the NVIDIA-NeMo/Megatron-Bridge repository. This work established scalable inference pathways and robust integration points, enabling smoother interoperability with existing Megatron-Bridge components and supporting future model variants. Leveraging deep learning and natural language processing expertise, the developer implemented a bridging layer in Python that broadens language model options and accelerates experimentation. The approach emphasized maintainable code and collaborative development, with clear sign-offs and cross-team contributions. This feature laid the groundwork for MoE-based cost efficiency and enhanced the repository’s capacity for scalable, flexible language model deployment.