I am a Principal Systems Architect with a 20-year history of engineering resilience.
My practice is defined by “Deep Stack” expertise. 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 distributed network topology 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 (Systems & Data)
Operational Background: Transitioning from physical infrastructure to software engineering, I spent years building scalable e-commerce platforms and transaction engines. This deep understanding of transactional logic led to my role as a Principal Lead Engineer within a 50-person cross-functional division, reporting directly to the VP of Architecture.
I engineered the technical backbones of production pipelines, orchestrating the complex data flow between Media Creators, Analysts, and DevOps teams. My focus shifted to Data Logistics: curating high-volume datasets, migrating legacy infrastructure, and engineering the ETL (Extract, Transform, Load) protocols that turned raw data into business intelligence.
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. I design systems where the AI is not just a chatbot, but an orchestrated component with secure, low-latency access to your data and business logic. I apply Formal Methods (TLA+) to these architectures, treating AI agents with the same strict reliability standards I applied to physical networks.
The Philosophy: Lean Architecture
Project Economics: 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 (FinOps): 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.
Petru Mirzenco
Principal AI Systems Architect
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