Worked on stability improvements for rotary embeddings in the jeejeelee/vllm repository, focusing on enhancing reliability for ROCm-based deployments. Addressed a critical runtime issue by implementing a fallback mechanism in Python and PyTorch, ensuring that when the flash attention rotary grid exceeded HIP per-dimension limits, the system would safely revert to native PyTorch operations. This approach prevented kernel launch failures for long sequences and large batches, supporting robust inference on ROCm GPUs. Demonstrated backend development expertise by validating HIP grid constraints and routing computation through a safe path, ultimately improving production readiness and cross-architecture compatibility for rotary embedding workflows.