For the UC Berkeley (M.Eng. EECS) x Siemens Capstone project, we address the problem where commercial buildings account for 30–40% of global electricity consumption, yet most operate on fixed energy schedules that ignore how grid carbon intensity fluctuates hour by hour. Our project, Carbon-Aware Energy Optimization Platform, addresses this gap by building a full-stack software platform that integrates Siemens Building X telemetry with real-time carbon-intensity and weather data to intelligently shift flexible building loads — like HVAC — toward lower-carbon windows. The optimization engine uses Mixed-Integer Linear Programming and Model Predictive Control to generate energy schedules that minimize operational emissions without requiring new physical infrastructure. A Python FastAPI backend drives the scheduling logic while a React dashboard lets operators visualize and compare baseline versus optimized cumulative CO₂ emissions in real time. The result is a functional prototype demonstrating how forecast-driven carbon intelligence can be practically embedded into commercial building operations.