Orlando Trejo is an engineer and researcher working at the intersection of digital engineering and artificial intelligence. With a PhD and a Professional Engineer license, he brings both academic rigor and practical experience to the challenge of modernizing how we design, build, and operate engineered systems.
Trained at Stanford, Orlando developed a deep appreciation for structured thinking and interdisciplinary problem-solving. His work spans model-based systems engineering (MBSE), digital twins, AI-augmented design, and the automation of engineering workflows.
DIGITALENG × AI grew out of a conviction that digital engineering knowledge is too often locked in dense government documents and vendor marketing. Orlando created the DGTLENG course catalog to make these concepts accessible, structured, and actionable — the kind of resource he wished existed when he started in the field.
When he's not building courses or prototyping AI-powered engineering tools, Orlando is exploring how large language models and reinforcement learning can be applied to real engineering problems — not as replacements for human judgment, but as amplifiers of it.