Worked on the vllm-project/aibrix repository to enhance observability and deployment reliability by implementing end-to-end tracing through upstream x-request-id preservation. Leveraged configuration management and Kubernetes skills to introduce a YAML-level filter in the gateway configuration, ensuring that request IDs are consistently propagated across service boundaries for improved traceability and faster root-cause analysis. Updated the deployment configuration for the metadata service to decouple it from external inference engine wiring, allowing for independent redeployments and reducing operational risk. Focused on infrastructure as code and DevOps practices, the work emphasized maintainability and resilience in cloud-native deployment environments without introducing new bugs.