Enterprise IT operations gave me the instincts. 18 months of applied AI engineering gave me the tools.
I spent five years managing global enterprise platforms under tight SLA clocks. In 2024, I channeled that debugging moat into software engineering: building Next.js, Expo, and multi-agent systems from the ground up. This is that story.
My path through enterprise IT wasn't handed to me. I started at the very bottom as a 3rd-party L1 support agent and earned my way up. Across two companies and five years, I went from answering tickets to owning operations: progressing through SME, Team Lead, and Acting Floor Manager, training entire support floors, and launching new service verticals under live SLA pressure.
That grounding taught me how systems fail under load, how automation compounds over time, and how to communicate technical failures clearly to stakeholders. Those instincts now inform every AI system I build.
Since early 2024 I have been learning AI engineering end-to-end and applying it to real systems, not toy demos. The goal was deliberate: take five years of enterprise IT operations knowledge and rebuild it for the AI-native era. That produced four production systems (two live, two in private beta), and to share the architectures publicly, I extracted the actual core engines and pipeline components from that commercial code into a suite of 14 open-source repositories: real, production-ready code rather than simplified mock models.
I'm not looking for just any engineering seat. I bring something most newcomers to AI can't: half a decade of watching real enterprise systems break under load, and 18 months spent learning to build so they don't. I want a team where that operational depth and systems thinking are assets, not background noise.
“Five years of enterprise IT operations is a debugging moat. I spent that time tracing failures across global SAP and ServiceNow stacks under tight SLA clocks. That precision maps directly into building and deploying AI systems that actually hold up.”