Hello, I'm
Patrick Pichardo
Full-Stack Engineer & Backend Architect
End-to-end engineer. I own the full stack — from schema design and backend architecture to deployed frontends and cloud infrastructure — and build systems that hold up in production.
About
I'm a full-stack engineer with a strong focus on backend architecture, DevOps, and scalable systems. I leverage LLM-based tools to accelerate technical research, debugging, documentation, and test generation — improving development velocity and code quality without cutting corners.
My recent work includes architecting a full-stack enterprise CRM and workflow automation platform — deployed on AWS ECS Fargate with a GitLab CI/CD pipeline, 6-tier role-based access control, Redis caching, and real-time third-party integrations — that was recently acquired. I now work full-time at the acquiring company, continuing to build on the same platform and infrastructure.
I hold a B.S. in Economics with certificates in Elements of Computing and Applied Statistical Modeling from UT Austin — a combination that lets me think clearly about both the systems I build and the business problems they solve.
Education
The University of Texas at Austin
B.S. Economics · Certificate in Elements of Computing · Certificate in Applied Statistical Modeling · 2022 – 2025
The University of Texas at El Paso
Transferred · 2021
Languages & Frameworks
Databases
Cloud & Infrastructure
AI & Data
Experience
Philosophy
My background is in economics, computer science, and applied statistics — fields that ultimately deal with the same problem: allocating scarce resources efficiently and making decisions under uncertainty. David Ricardo's theory of comparative advantage describes trade, but it also applies to how individuals allocate their time. When Ethan Mollick showed that knowledge workers using AI complete tasks significantly faster, the implication was clear: the marginal cost of learning has dropped, and effort should shift toward higher-judgment work.
I use AI as a learning multiplier. It allows me to move through unfamiliar domains faster — not by skipping understanding, but by reaching the parts that require judgment sooner. At the same time, it eliminates low-leverage work: boilerplate, syntax recall, repetitive debugging, and documentation lookup. Those tasks don't require deep thinking, so I offload them.
The result is a simple operating model: I spend my time either building, or learning so I can build better. Everything else is optimized away.
Knowledge Acquired
r = 7 · α = 1.8
All three lines start at the same baseline. The flat line represents no active learning. Traditional learning grows steadily but linearly — the same amount each month. AI-augmented learning compounds: each thing you learn lowers the cost of learning the next, so the gap widens every month rather than staying fixed.
Resources
What I've learned from and keep coming back to — architecture fundamentals and the AI tooling I use to learn and build faster.
Designing Data-Intensive Applications
Martin Kleppmann
The definitive guide to building reliable, scalable, and maintainable systems. Covers databases, replication, partitioning, and distributed system tradeoffs.
Clean Architecture
Robert C. Martin
SOLID principles and structural patterns for building systems that are easy to change, test, and understand over time.
ByteByteGo
Alex Xu
Visual system design breakdowns — how real systems like YouTube, Uber, and Twitter are built. Essential for internalizing large-scale architecture patterns.
The System Design Primer
Open-source GitHub repository covering every core system design concept with diagrams and examples. A go-to reference before designing anything at scale.
The Pragmatic Programmer
Hunt & Thomas
Engineering philosophy over syntax — how to think about code quality, maintainability, and your career as a craftsperson.
Contact
If you have a project, need AI or software consulting, want to join networks, or just want to talk — reach out. Best through email or LinkedIn.