Khalid Shaheed

Khalid Shaheed serves as an Artificial Intelligence Engineer at Capitol Technology University, where he is responsible for the architecture, design, and strategic development of the Capitol Artificial Intelligence Learning and Innovation Environment (CAILIE). In this role, he oversees the technical setup and ongoing management of our advanced AI infrastructure, ensuring that the lab meets the research and educational standards of the university's students, faculty, and industry partners.  

Starting in the digital dawn of Apple (Macintosh,1984) to the current "AI Renaissance", Shaheed viewed computer systems as puzzles to solved, which has fueled a lifelong curiosity for the digital world. His prior work experience spans Media/Big Tech (WBD, Comcast), Federal Defense (DoD, US Courts, Census), and Enterprise Software (Software AG, webMethods, JDA, Stride (K12 inc)). At Capitol Tech, the CAILIE team ensures that we are poised to usher in the transition from operating computers to collaborating with an intelligence of our own design. 

Shaheed joined Capitol Tech because of the opportunity to build something from the ground up with a focus on education, innovation, and impact. He began developing the vision for CAILIE by creating a 3D model of the future lab environment and outlining how it could operate to support the university’s AI initiatives. His work focuses on continuing to build the foundation needed to help students and faculty move beyond simply operating technology toward collaborating with intelligent systems of their own design. Through CAILIE, Shaheed creates an environment where experimentation, exploration, and persistence drive innovation. 

For students pursuing careers in AI and technology, Shaheed encourages curiosity, persistence, and maintaining the ability to think independently. He advises future professionals to use AI to enhance their work while continuing to develop their own technical skills, creativity, and problem-solving abilities. 

Areas of Expertise

• Artificial Intelligence
• Digital Infrastructure
• 3D Modeling and Simulation
• Machine Learning
• Computer and Network Engineering
• Large Language Models (LLMS)