I am a Principal Systems Architect with a 20-year history of engineering resilience.
My practice is defined by “Deep Stack” expertise. I do not simply write code on top of abstractions I do not understand. My career has evolved linearly from the physical network layer to high-volume data logistics, and finally to applied Artificial Intelligence.
To engineer reliable AI, one must first understand the machine it runs on.
Phase I: The Foundation (Physical Networks)
The ISP Origin My architectural philosophy is rooted in physical reality. Early in my career, I founded and engineered a physical Internet Service Provider (ISP).
This was not a theoretical exercise. I managed the deployment of network topology for 100+ nodes under hostile regulatory conditions. I dealt with signal latency, physical hardware failures, and monopoly competition. This experience established my baseline for all future work: Uptime is a survival metric.
Phase II: The Logic (Enterprise Data)
Operational Background Transitioning to the US Enterprise sector, I served as a Principal Lead Engineer within a 50-person cross-functional division, reporting directly to the VP of Architecture. This unit integrated Media Creators, Data Analysts, and DevOps engineers into a single delivery stream.
My role was the technical backbone of this ecosystem. I was responsible for Data Logistics: curating high-volume datasets, migrating legacy infrastructure, and engineering the ETL (Extract, Transform, Load) pipelines that powered the division’s output. I learned how to structure complex data so it delivers actual business value to stakeholders.
Phase III: The Intelligence (Applied AI)
Generative Architecture Today, I apply that rigor to Artificial Intelligence. Because I understand the physical constraints of the server and the logical flow of the data, I do not build superficial “wrappers.”
My practice deploys Native AI Architectures on Google Cloud. I design systems where the AI is not just a chatbot, but an orchestrated component that has secure, low-latency access to your inventory, business logic, and structured data.
The Philosophy: Lean Architecture
Project Economics & FinOps Experience has demonstrated that adding headcount to a complex system often increases friction rather than velocity. I operate on the principle that complex problems are best solved by a small nucleus of senior experts, not large teams of junior developers.
Financial Discipline I do not treat Cloud resources as infinite. A core component of my architectural review is FinOps (Financial Operations). I design systems that are not only performant but financially sustainable, ensuring that compute costs do not scale disproportionately to revenue.