Worked on stabilizing the deployment environment for the vLLM inference service in the red-hat-data-services/vllm-gaudi repository, focusing on improving reliability and reproducibility. Addressed environment configuration by updating the Dockerfile to pin the PyTorch version and ensure correct pandas installation, which reduced inconsistencies across deployments. Standardized the server script’s model variable naming to support consistent model scripting and onboarding. Utilized Docker, Python packaging, and shell scripting to enforce explicit package versions and streamline configuration. These changes minimized startup and runtime issues, making the deployment process smoother and reducing troubleshooting time for operations teams managing the vLLM service.