Brandon LEE
AI Archiect Lead at F5
Mountain View, California, United States
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As a hands-on AI leader in MLOps, On-Device AI, and Generative AI, I have built and led high-performing teams at top-tier technology firms through complex technical challenges. I design and deploy AI/ML products and strategies that deliver measurable business value while integrating seamlessly into existing product infrastructures. I bring strong executive acumen, aligning technical innovation with business priorities, translating complex AI concepts for senior leadership, and driving decisions that balance scalability, risk, and ROI.
At F5, I work in the AI Center of Excellence, where I prototype proof-of-concept AI solutions and integrate them with product teams. I built an AI system that brings intelligence to application infrastructure at fleet scale, automatically interpreting configurations to surface insights that are otherwise hard to extract manually across large deployments. I also prototyped work augmenting F5 Distributed Cloud WAAP with specialized AI classifiers for web and API protection, including LLM-era threats like prompt injection, under a safety-first design where no single signal can autonomously block traffic.
At Rockwell Automation, I developed AI-driven manufacturing solutions that improved user experience while reducing operational costs. At Microsoft, I enhanced ML workflows through automation and built training programs that bridged knowledge gaps across pre-sales and engineering teams. At Samsung SDS, I helped launch the first enterprise big data platform in the U.S.
My work has earned multiple recognitions, including HP's 2025 Innovation Speaker award, Rockwell's GenAI Hackathon win, Microsoft's Quarterly Award, and Best Speaker at Microsoft's Internal ML Conference. I also hold an MLOps patent for managing AI systems in industrial environments. Through collaborative leadership and mentorship, I foster open communication, creative problem-solving, and sustainable engineering practices that consistently drive team success.
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Reading the Mind of Your NGINX Fleet: A Hybrid Rule + ML Pipeline for NGINX Config Intelligence
Every NGINX config encodes intent — is this a reverse proxy? an API gateway? an SSL terminator? — but that intent is never written down. At fleet scale, nobody can tell you what your configs actually do. This session shows how we taught a machine to read them.
We classify NGINX server configs into eight canonical roles using a three-stage pipeline: parse the raw config into a structured directive tree with CrossPlane, then run two complementary classifiers against it — a precision-tuned rule engine that abstains when signatures are ambiguous, and an ML model that generalizes to configs the rules don't cover. A confidence-fusion layer blends them, auto-accepting confident calls and routing borderline ones to expert review.
The counterintuitive lesson: we deliberately didn't just throw an LLM at this. The hybrid beats either approach alone — keeping rule-level precision while recovering the coverage rules lack. You'll see why "negative evidence" (the absence of a directive) is often the strongest signal, how an LLM-as-judge eval validated the design, and how a closed expert-feedback loop turns review hours into measurable accuracy gains. Live, against real configs — including yours, if you bring one.
Brandon LEE
AI Archiect Lead at F5
Mountain View, California, United States
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