Stained Glass Engine (SGE) is a tool developed by Protopia AI for AI/ML engineers to protect sensitive data used in AI workloads. It works by creating Stained Glass Transforms (SGTs), which are privacy-preserving transformations applied during model training. SGE integrates into existing training pipelines without requiring modifications to base model code. Engineers wrap their existing loss functions, optimizers, and models using SGE's API calls. Internally, SGE uses PyTorch hooks to manipulate loss functions, manage data flows, and apply memory optimizations during SGT creation and training. The tool supports any PyTorch module, including Hugging Face Transformers, enabling it to be applied across a broad range of AI training loops. SGE exposes hyperparameters that allow users to control the balance between the strength of the privacy transformations and the utility of the original model, including control over stochastic transformation behavior. SGE is applicable in sensitive data environments, including financial services and defense contexts, where AI workloads must operate on private or regulated data without direct data exposure.