I'm a Staff Mobile Engineer and Flutter GDE with 15+ years of experience building high-performance mobile applications. Currently leading a team of 12 engineers on a financial app serving 3M+ users. My expertise spans Flutter, iOS, and Android development, with proven experience integrating and optimizing on-device AI/ML models (TensorFlow Lite) in production at scale.
As a Flutter GDE with 15+ years of experience, I've mastered the art of building exceptional mobile applications across Flutter, iOS, and Android platforms. My journey has taken me from native development to cross-platform solutions, always focusing on delivering high-quality, performant apps.
I've successfully integrated and optimized TensorFlow Lite models in production for features like face liveness detection, serving 3M+ users. My focus is on making AI work efficiently on mobile devices—optimizing inference speed, managing memory constraints, and maintaining the smooth, privacy-focused experience users expect.
The mobile ecosystem is deceptively deep. I believe that to truly master the nuances of performance and UX, one cannot afford to be a generalist.
"I'm not just a mobile developer—I'm an expert who understands mobile platforms deeply and delivers experiences that users love and businesses depend on."
Recognized by Google as a Flutter Developer Expert since December 2022. Actively contributing to the Flutter community through public speaking and technical blogging.
Leading a team of 12 mobile engineers on a high-security financial app serving 3 million users. Building with Flutter, iOS, and Android while integrating on-device AI capabilities using TensorFlow Lite.
Deep expertise in Flutter, iOS, and Android development. Mastered mobile internals including memory optimization, native platform integrations, and performance profiling for enterprise-scale applications.
Challenge: To enhance security for 3M+ users in a high-security financial application, we needed to implement real-time face liveness detection that could run entirely on-device without compromising user privacy or app performance.
My Role: As Staff Engineer, I led a 2-year initiative to integrate and optimize a TensorFlow Lite face liveness model into our production Flutter app. I architected native iOS and Android bridges using platform channels, optimized model inference performance (reducing latency by 40%, memory usage by 30%) across different device capabilities, and coordinated between data science (model training), mobile engineering (integration), and security (compliance) teams to deliver a seamless production solution.
Impact: Successfully deployed on-device AI that processes sensitive biometric data locally, eliminating server round-trips and maintaining user privacy while achieving real-time detection accuracy that met our security requirements across millions of daily authentications.
Challenge: Our financial app serving 3M+ users needed to maintain enterprise-grade performance while handling increasing feature complexity, strict security requirements, and variable network conditions across diverse device capabilities.
My Role: As Staff Engineer leading platform engineering, I architected and implemented comprehensive performance optimizations including intelligent API strategies, multi-layer caching, offline-first architecture, and server load reduction—all while maintaining strict security compliance for financial transactions.
Impact: Reduced app launch time by 40%, decreased API calls by 60% through smart caching, improved offline functionality for critical user flows, and significantly reduced server costs while maintaining zero security incidents.
Since becoming a GDE in December 2022, I've been actively contributing to the Flutter community through technical articles on benamorn.medium.com and speaking at tech conferences across Southeast Asia.
My talks cover Flutter development, mobile architecture, platform integrations, and recently, on-device AI implementation—sharing practical insights from real-world production experience.
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Nov 23, 2025
Exploring on-device AI by using Gemini Nano to create an AI-powered mascot on edge devices. Demonstrating how Flutter can integrate with on-device AI for personalized user experiences without server dependencies.
View Slides →Nov 1, 2025
Exploring on-device AI as a complement to cloud AI. While powerful models live in the cloud, many personal and responsive features benefit from running directly on the device—this talk covers practical implementation using TensorFlow Lite.
View Slides → View on Youtube →JUL 26, 2025
Why choose between native and Flutter when you can use both? This talk explores Flutter's Add-to-App feature, showing how to seamlessly integrate Flutter modules into existing iOS and Android projects to leverage the strengths of both approaches.
View Slides →I'm always open to discussing mobile development, Flutter, iOS, Android, on-device AI, or potential collaborations.