Honest about what I've earned, and what I'm still working toward.
A clear split between certifications I'm actively preparing for, university coursework I've completed, and topics I'm currently exploring. Nothing is presented as earned unless it actually is.
Certifications I'm preparing for.
Not yet earned. I'll update this page when they are.
AWS Certified Solutions Architect — Associate
Designing resilient, cost-optimized, and secure architectures on AWS. Maps directly to the serverless work I do day-to-day — Lambda, API Gateway, DynamoDB, Cognito.
AWS Certified Developer — Associate
Developing, deploying, and debugging cloud-native applications on AWS. The companion to the Architect cert — same services, builder's lens.
What I've focused on at the master's level.
M.S. Software Engineering at Western Governors University, with an AI emphasis — nearing completion. The list below is the through-line, not a transcript.
Software Architecture & Design
Architectural patterns for systems that have to keep working — distributed services, API boundaries, trade-offs between consistency and availability, and when to choose the boring option.
Applied AI & Machine Learning
Where ML actually earns its place in software — model selection, evaluation, and integration into production systems rather than notebook demos.
Advanced Software Engineering
Engineering practices for production-grade systems: testing strategy, observability, deployment patterns, and the discipline of shipping software that survives contact with users.
Secure Software & Data Systems
Auth, access control, encryption boundaries, and the data layer — the parts that quietly fail in real applications if you don't design for them up front.
Undergraduate foundations — data structures, algorithms, full-stack, ML fundamentals — completed earlier at Cal Poly San Luis Obispo (B.S. Computer Science, 2023).
Working notes and experiments.
Less formal than coursework — topics I'm exploring in real time. Writeups land in the Learning Lab as they take shape.
Small applications on top of LLM APIs and ML fundamentals from the ground up. Interested in where AI is genuinely useful in production software.
Auth patterns, XSS, SQL injection, and the security surface of modern web apps — what actually shows up shipping real systems.
Practical performance work — measuring latency, identifying bottlenecks, reducing time-to-resolution on production issues.