Staff Mobile Engineer
Specializing in
On-Device AI.

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 effort to integrate a complex TensorFlow Lite model into our production Flutter app. This involved architecting the native iOS/Android bridge using platform channels, optimizing model inference performance across different device capabilities, and coordinating between the data science team (model training), mobile engineers (integration), and security teams (compliance) to ensure a seamless and secure user experience.

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: A 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 initiatives, I architected and implemented comprehensive performance optimizations including intelligent API call strategies, multi-layer caching systems, offline-first architecture, and server load reduction techniques—all while maintaining strict security compliance for financial transactions and sensitive user data.

Impact: Reduced app launch time by 40%, decreased API calls by 60% through smart caching, improved offline functionality for critical user journeys, and significantly reduced server costs while maintaining zero security incidents and improving overall user experience scores.