Ali ParandehGheibi

I’m a passionate technologist with over 13 years of experience building and scaling AI-driven enterprise security platforms — from 0→1 prototypes to globally deployed systems. My career has consistently lived at the intersection of deep technical innovation and real-world operational complexity, where clarity is scarce and execution matters most.

Previously, I was a Sr. Distinguished Engineer at Palo Alto Networks, where I architected and built the company’s AIOps for NGFW product from the ground up. That work — now part of Strata Cloud Manager — involved building enterprise-grade AI to solve complex operational and support issues, typically done by human experts. That involved building agents that automate root cause analysis, predict outages, handle support cases, and strengthen customer security posture.

Before that, I was a founding engineer at Tetration Analytics (acquired by Cisco). There, I built core systems for zero-trust microsegmentation, including the first data pipelines, ML-driven application dependency mapping, policy management workflows, and E2E regression frameworks that enabled enterprise-grade reliability in on-prem and air-gapped environments.

I earned my PhD in EECS from MIT, which grounds my approach in first-principles thinking and equips me to solve ill-defined, high-complexity problems. I have published over 30 peer reviewed papars and awarded over 40 Us/International patents.

What drives me is building: not just technology, but teams, products, and cultures where experimentation, rigorous execution, and thoughtful risk-taking can thrive. I’ve spent my career turning abstract ideas into production-grade systems that protect critical infrastructure and reduce complexity for customers.

After wrapping up my tenure at Palo Alto Networks, I conducted independent research on agentic systems together with a team from MIT. We built Glia: A Human-Inspired AI for Automated Systems Design and Optimization, which is a multi-agent system capable of designing novel systems algorithms, such as GPU request scheduling and auto-scaling. Algorithms designed by Glia perform on par with a PhD-level systems researcher at a fraction of the time via long-term design space exploration. Glia reasons about the system code and architecture, forms hypotheses, writes code, conducts numerical experiments, and further refines its approach by reasoning about the past trajectories. This work made me even more excited about the endless possibilities that agentic systems can unlock in enterprise settings.