I build the whole system,
and I keep it running.

The arc

Each role handed me more to own. The climb, in order:

• Healthcare compliance. Led compliance initiatives at Humana (Medicare Advantage), automating audit workflows and data validation under HIPAA and Medicare Parts C and D.
• Quality engineering. Built automated test frameworks and data-model validation. This is the discipline of proving a system does what it claims before it ships.
• Data engineering. Designed and built pipelines, BI, and API integrations for a major public utility (PRASA via TrueNorth), and led data-governance work across its analytics.
• AI and automation leadership at Optum (UnitedHealth Group). I now lead the AI/LLM automation workstream and the migration of legacy systems into modern, ML/AI-driven platforms. An in-house LLM tool I built is in production, automating first-draft BRDs and data-dictionary context. I also designed and taught an Excel-based analytics course that got non-technical supervisors and managers working with data directly.

The throughline is simple. Compliance taught me what “correct” has to mean in a regulated, high-stakes domain. Quality engineering taught me how to verify it. Data engineering and AI leadership are where I set direction and drive delivery now.

Where I work, and who I build for

I’m based in Puerto Rico and work remotely with U.S. teams. My own products are built for the PR market first: bilingual (EN/ES), local data sources, and the Act 60 context that matters to business clients here. Most of my time goes to building and running the systems; the rest to the people who use them.

How I deliver, and where the judgment comes in

Here’s the part a resume doesn’t show. Everything in my portfolio (a live real-estate marketplace, a procurement-intelligence product over 34 government sources, and a multi-tenant operating system for food businesses) I took from architecture to live production, and I run today.

I use AI tooling heavily for implementation, the way any modern engineer does. What does not delegate is the architecture, the judgment calls and the verification — and that is where most of the work lives. A few examples:

• An adversarial QA pass on StreetOS that turned up dozens of real defects in a suite that had been fully green.
• A completeness audit of the LicitaPR data backend that caught ~80K silently-missing records — re-crawled, not served partial.
• An authorized Nidopr security audit. 21 findings, posture strong.
• A tempting fraud thesis in the procurement data, tested hard and killed before it could ship. A less skeptical process would have published it.

That last one is the whole point. AI will happily generate a confident, wrong answer. My job is to refuse to ship it until it has been tested hard enough to break.

The honest line

AI does not replace the engineer; it multiplies one. It lets me get from a problem to a shipped, running product fast, while still owning the architecture, the judgment calls and the verification that decide whether the work is true. That is what a client gets from Vizlogic, and what I bring to a team.

That is the skill I am bringing.