Platform engineering is essential for scaling generative AI and overcoming the deployment challenges of Large Language Models (LLMs). Many organizations struggle to transition from LLM prototypes to production due to complex operational requirements. These include the need for specialized knowledge in machine learning, DevOps, and cloud infrastructure. As a result, data science teams often face delays, waiting for assistance from central teams, which undermines the agility that LLMs are designed to provide. The article emphasizes how a standardized Internal Developer Platform (IDP) can streamline LLM deployment. By abstracting the complexities of the underlying infrastructure, platform engineering enables developers to manage and scale models independently. Key challenges in LLM operations include infrastructure provisioning, model packaging, deployment automation, observability, and security. For Retrieval Augmented Generation (RAG) deployments, the complexity increases further due to the need for managing vector databases and data synchronization. Without a well-defined platform, these tasks can create significant operational drag. To address these challenges, the article outlines a production-ready LLM deployment stack composed of three layers: infrastructure management with HCP Terraform, CI/CD automation using GitHub Actions, and a developer-friendly interface through Port. HCP Terraform allows platform engineers to define infrastructure as code, while GitHub Actions automates the deployment process triggered by self-service requests. Port serves as a unified interface for developers to initiate deployments with ease, enhancing the self-service experience. This structured approach transforms a traditionally cumbersome deployment process into a more efficient, repeatable workflow that aligns with organizational goals.