Alejandro Sanabria

Forward Deployed Engineer @ Wonderful.ai · Mobile Platform Leader · Former EM

Forward Deployed Engineer at Wonderful.ai and a mobile platform leader with deep iOS and Android experience.

My core chapter is mobile: 8+ years building products and platforms at Platzi, growing from one of the first mobile engineers into leading mobile engineering across iOS and Android. Before that, 4+ years as co-founder of Brand Spa, leading development and shipping full-stack products directly for B2B clients — the same job an FDE does today: sit with the customer, understand the real problem, and deliver working systems. That foundation shapes the lens I bring to FDE work: customer problems, architecture, delivery systems, code quality, practical AI workflows, and fast mobile execution when the product needs it.

My longest and deepest chapter was at Platzi, where each phase taught me something different about how mobile software gets built at product scale.

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.

A shorter Baubap chapter reinforced the same pattern from an engineering management seat: align product, platform, and delivery quickly enough to get a first iOS version moving in a compressed timeline.

Before mobile, I co-founded Brand Spa, a digital agency where I spent 4+ years leading development and building full-stack products for B2B clients — quoting systems, CMS platforms, e-commerce flows, website builders, and internal tools that worked in production. Every project meant sitting with a client, translating their problem into architecture, and owning delivery end-to-end. That was forward deployed engineering before I had the name for it.

Now I'm a Forward Deployed Engineer at Wonderful.ai, closing the loop: the client-facing builder instincts from Brand Spa, the platform depth from Platzi, and AI systems as the new delivery surface.

struct EngineeringScope {
    let current     = "Forward Deployed Engineering @ Wonderful.ai"
    let platforms   = ["iOS", "Android", "Web"]
    let stack       = ["Swift", "Kotlin", "React Native", "TypeScript"]
    let focus       = ["Customers", "Architecture", "People", "AI Workflows"]
    let approach    = "Go deep on each one, know when to switch"
}

Technical side:

• Helped design streaming and dynamic content architectures at scale
• Contributed to internal libraries shared across the mobile platform
• Helped define standards that keep both iOS and Android codebases healthy
• Shipped on-device AI features with Apple Foundation Models alongside my team
• The mobile team I helped build was recognized by Apple as an innovation example for Platzi
• Architectural judgment across React Native, Swift, and Kotlin — not locked to one platform
• Built full-stack B2B products from architecture decisions to production delivery
• Created practical AI-assisted workflows for planning, review, validation, screenshots, and release operations

People side:

• Helped scale and structure mobile teams across iOS and Android
• Mentored engineers through career transitions with high standards
• Shaped a review culture with the team where quality is the norm, not the exception
• Aligned clients, product stakeholders, designers, and engineers around concrete delivery decisions

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.

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.

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.

FDE work               → close to customer workflows, product needs, AI systems, implementation
Architecture workflows → breaking ambiguous work into implementation plans and trade-offs
Review loops           → using AI to preserve context, check assumptions, verify output
Delivery tooling       → automating screenshots, App Store work, and release steps
Product systems        → building apps like Cifro where AI supports useful product behavior

The goal is not AI for its own sake. The goal is better engineering judgment, faster feedback, and working systems.

• Cifro — Published private-by-design iOS finance app built with SwiftUI and Apple Foundation Models. On-device AI for transaction parsing, budget suggestions, and daily insights — no accounts, no cloud, no data sharing.
• ios-architect-skill — Reusable workflow patterns for iOS architecture, implementation planning, review, and validation. Encoding engineering judgment into repeatable systems, not prompt tricks.
• appstore-tools — App Store Connect metadata and build upload automation in TypeScript.
• screenshots-ios — Deterministic iOS simulator screenshot capture for App Store assets, regression checks, and CI pipelines.

• Quality is a multiplier, not a tax. Investing in architecture pays back in velocity every sprint.
• Lead from the code, not above it. An EM who can't evaluate a technical trade-off is managing blind.
• AI becomes useful when it lands inside a workflow. The hard part is connecting model behavior to a real job, team, and outcome.
• AI should augment judgment, not replace it. The goal is better engineering, not just faster output.