Portfolio · Manila, Philippines
Senior AI-Native Mobile Engineer
I’m a senior mobile engineer with 12+ years of software engineering experience and 10+ years focused on Android — deepest in Kotlin and Android, with production iOS and Flutter work and Android platform compatibility engineering.
I work end to end: architecture, offline-first data, performance, testing, and store delivery across native Android, native iOS, and Flutter.
AI is part of how I engineer. I use agent-assisted workflows to investigate unfamiliar codebases, plan implementations, analyse logs and source, generate targeted tests, and accelerate implementation — then validate the result through code review, tests, and runtime verification.
Selected work
Six case studies covering the product, my contribution, and the engineering problems involved.
An AI-powered workout and training product built around adaptive plans, progress tracking, and Lex — a conversational coaching experience integrated into the mobile app.
A native field-operations platform for technicians covering bookings, safety workflows, job management, parts, media capture, invoicing, and dependable offline operation.
A multilingual money-tracking application for income, expenses, and debts, designed for users across Cambodia and the Philippines and shipped to Android and iOS from a shared Flutter codebase.
Large-scale Android travel experiences supporting planning, booking, flight information, and day-of-travel journeys.
Multimodal trip planning that combines public transport, rideshare, cycling, walking, and other mobility options into a single journey.
Vehicle and fleet intelligence for live monitoring, trip history, location, and operational information across connected fleets.
AI-native engineering
Two kinds of AI experience matter in my work: shipping AI-enabled product experiences, and using AI as part of the engineering loop itself.
On Flex, I contributed to the native Android experience around Lex, the product’s conversational AI coach. The product supports conversational input and structured workout-generation experiences integrated directly into the mobile application.
My contribution is on the mobile product experience — not model training or LLM infrastructure.
I use AI agents as engineering tools across unfamiliar codebases — to investigate source, plan implementations, correlate logs, generate targeted tests, and accelerate development.
AI accelerates investigation, implementation, and verification; I validate the result through code review, tests, and runtime verification.
Platform engineering
Alongside product development, I work below the app layer on Android compatibility — investigating failures against AOSP source, device logs, CTS results, and CI/test-farm evidence.
Android CTS execution and triage through Tradefed across Android 14 and Android 15.
Investigating compatibility failures against AOSP source to isolate whether the issue sits in the test, Android framework behaviour, device implementation, or environment.
ADB-driven inspection, log capture and correlation, reproduction, and device/rack-level troubleshooting.
Comparing CI and device-farm results to distinguish genuine regressions from environmental failures, flakes, and infrastructure issues.
Engineering depth
A practical toolkit for reliable mobile products, from architecture and offline data through testing, delivery, and production support.
Kotlin, Java, Jetpack Compose, Android SDK, Coroutines, Room, WorkManager, RxJava/RxKotlin, Hilt/Dagger.
Swift, UIKit, SwiftUI where supported, Core Data, native iOS delivery, and Android/iOS feature parity work.
Flutter, Dart, Provider, Riverpod where supported, Firebase, and coordinated Android/iOS delivery.
MVVM, MVP, Clean Architecture, REST, GraphQL, offline-first caching, SQL, MongoDB, background sync and resilient mobile data flows.
JUnit, Mockito, MockK, Kotest, Robolectric, GitHub Actions, release management, analytics, crash monitoring, performance investigation.
Have a product in mind?
