I'm the person who makes sure code gets from a developer's machine into production, and stays running once it's there.
I'm RHCSA-certified, reflecting a Linux foundation I built early and have relied on ever since. I've built and maintained CI/CD pipelines using Jenkins, GitHub Actions, and Bitbucket Pipelines; deployed and managed applications on AWS, Azure, GCP, and Oracle Cloud Infrastructure; worked extensively with Docker and Docker Compose; and, over the last year, added backend development in Python and FastAPI to that skill set.
I automate because manual steps are where mistakes and inconsistency creep in, especially under time pressure. A well-designed pipeline removes that risk entirely for the cases it covers. Production is where all of this gets tested for real — I've learned to treat "it works on my machine" as the start of a question, not the end of one.
Backend development complements infrastructure work rather than competing with it. Understanding how an API is actually built makes me a better DevOps engineer, and understanding how it gets deployed and monitored makes me a better backend engineer.
I'd rather ship something slightly later and have it hold up in production than ship on time and spend the following week firefighting.
If something I built breaks, I want to be the one explaining why — not the one waiting to be told.
I default to documenting decisions and procedures in writing, especially with international clients working across time zones.
If I don't know why something is failing, I say so and start investigating rather than guessing out loud.
How I use AI tools
AI-assisted development has become a normal part of how I work. Here's how I actually use it — described honestly, without overstating or downplaying it.
I use ChatGPT and Claude when I’m stuck, describing the symptom and what I’ve already ruled out — to check whether I’m missing an obvious angle, not to skip the diagnostic process.
Cursor AI is where I get AI assistance directly inside my editor, alongside code, infrastructure config, and docs — faster boilerplate for pipelines and scripts, so I can focus on architecture and security-relevant decisions.
AI tools help produce a first draft of technical write-ups and procedures, which I then edit for accuracy against what I actually built.
A second pass after my own review, to catch inconsistent error handling or edge cases — particularly in backend code written under time pressure.
None of this replaces understanding the system I'm working on. I don't deploy configuration or ship code I can't explain, because I'm the one who has to support it in production afterward.