Skills · Tools & learning
What I work with.
These are the tools and approaches I use in professional work and personal projects. The links below show where I’ve put them to use.
AI quality
I’ve evaluated LLM outputs against acceptance criteria and tested edge cases across model versions. My personal projects explore RAG checks, judge calibration, traces, and agent reliability.
Python · Docker · LangGraph · DeepEval
See EvalHarness ↗Test automation
I build API, integration, and end-to-end checks, using Page Object Models where they help keep UI tests maintainable. I prioritize regression checks by risk and record failures so they can be reproduced.
Playwright · Pytest · Selenium · Postman · Allure
See PG Original ↗Engineering & data
I use Python to automate business workflows, connect APIs, and validate financial data. My personal projects also include TypeScript tools and SQLite storage for results and test records.
Python · JavaScript · TypeScript · SQL · SQLite
See Cartographer ↗Delivery & reproducibility
I use Docker, versioned test data, and CI workflows to make checks easier to repeat. My project work also includes AWS Lambda for running automation.
Git · GitHub Actions · Docker · AWS Lambda
See Evalstand ↗Education · Continuous learning
What I’ve been learning.
Courses and certifications that complement my work in testing and AI. Open a certificate for the full details, or follow its verification link.
AI & data
Google · Coursera
Google Advanced Data Analytics Professional Certificate
Version 2 · 16 June 2026
Agent engineering
LangChain Academy
Introduction to Agent Observability & Evaluations
Foundation · 24 June 2026
QA & delivery
Languages
University education
Pharmacy · Universidad Nacional del Sur