Developed and delivered a foundational MLOps deployment for the Softala-MLOPS/oss-mlops-platform repository, focusing on integrating Kubeflow and KServe to streamline end-to-end machine learning workflows. The work centered on creating reusable Kubernetes manifests and configuration files using YAML and Shell scripting, enabling standardized, one-click deployments across the OSS platform. Addressed stability in Kubeflow Pipelines by implementing targeted fixes, which improved the reliability of the Kubeflow and KServe integration. Comprehensive documentation and setup guidelines were provided to facilitate onboarding and enhance developer productivity. Demonstrated strong skills in Cloud Computing, DevOps, and Kubernetes while emphasizing reproducibility and platform standardization.