Alejandro Sanabria is a hands-on engineering leader with 8+ years of experience turning complex product and technical problems into shipped iOS and Android systems. I grew from one of Platzi's first mobile engineers into leading mobile engineering across both platforms, while staying close to architecture, code quality, product trade-offs, stakeholder alignment, and delivery execution.
Recently, I have been applying AI to the way real engineering work gets done: breaking down ambiguous problems, preserving context, reviewing trade-offs, validating implementation, and automating repetitive delivery steps.
My longest chapter was at Platzi, where each phase taught me something different about how software gets built.
Joined as the second mobile engineer. Small team, big product, everything to figure out. That's where I learned Swift and SwiftUI deeply — and where I learned that quality isn't the opposite of speed; it's what makes speed sustainable.
Then the platform outgrew its stack. I was part of the team that migrated from React Native to fully native apps — Swift on iOS, Kotlin on Android. Working across all three paradigms taught me what each approach actually costs and where each one breaks.
The migration wasn't about picking a side. It was about understanding the trade-offs deeply enough to make the right call for the product.
Leadership came next. Not away from engineering — deeper into it. Helped scale the mobile platform across both iOS and Android, and pushed to turn technical debt reduction from a wish list into a discipline.
Now I'm applying the same builder mindset to AI workflows: practical systems that help teams preserve context, review trade-offs, validate implementation, and ship with less repeated setup work.
The two sides aren't separate. The best technical decisions come from understanding the team, the product, and the business context — and the best teams are built around a clear technical vision that compounds product outcomes.
Android scope: I've led it directly — shared architecture decisions, critical unblocking, and unified execution standards. iOS and Android move best as one team, not two silos.
I'm applying AI to the parts of engineering where context, review, and repetition usually slow teams down.
The work is practical: tools and workflows I use to plan, build, verify, package, and release real software.
The goal is not AI for its own sake. The goal is better engineering judgment, faster feedback, and working systems.
Reusable workflow patterns for iOS architecture, implementation planning, code review, validation, App Store operations, and screenshot generation. The focus is not prompt tricks — it's encoding practical engineering judgment into repeatable systems.
ios-architect-skillA published private-by-design iOS finance app built with SwiftUI and Apple Foundation Models. Cifro uses on-device AI for transaction parsing, budget bucket suggestions, daily insights, and a review ritual — no accounts, no cloud, no data sharing.
View on App StoreCommand-line tooling that supports the mobile delivery workflow: App Store Connect metadata and build upload automation, plus deterministic iOS simulator screenshot capture for App Store assets, regression checks, and CI pipelines.
appstore-tools screenshots-ios