Summary
Jonathan Baraldi is an AI safety researcher, systems architect, and instructor with 11 years of experience building cloud-native, secure, and scalable ML infrastructures. He is the founder and lead research engineer behind the Transparent AI Suite and author of a formalized methodology for Knowledge Provenance Maps, currently tied to a U.S. patent application. Jonathan bridges mechanistic interpretability research with pragmatic engineering—translating “MRI/fMRI”-style analyzers into deployable, auditable tools for enterprises. His background spans senior DevOps and cloud architecture roles at PwC, ADP, and multiple startups, where he operated Kubernetes, AWS, and CI/CD at scale. An active GitHub contributor, he combines backend Go work for Kubernetes deployments with hands-on DevOps automation and tooling showcased in public repos and course material. Based in Brasilia, he uniquely blends product-focused research with real-world operational rigor to make AI systems transparent and auditable.
11 years of coding experience
10 years of employment as a software developer
Bachelor's degree, Advertising, Bachelor's degree, Advertising at Unisinos
English