• Designed and developed robust iOS applications integrating AI-driven disease detection models, enhancing healthcare solutions.

• Built modular Swift/SwiftUI components leveraging CloudKit & CoreData, increasing scalability and reducing code duplication by 40%.

• Designed and integrated a real-time data synchronization system using CloudKit, resulting in a 98% reduction in data conflicts.

• Integrated machine learning models for real-time disease detection within user-friendly interfaces.

• Spearheaded AI model integration, reducing disease detection time by 50% compared to traditional methods.

• Migrated legacy platform to modern architecture, improving app load times by 30% and increasing user retention.

• Implemented RESTful API integrations, facilitating seamless backend communication.

• Refactored codebases using MVVM architecture, improving code readability and maintainability.

• Enhanced application speed and functionality through performance optimizations.

• Implemented comprehensive UI automation test using XCTest/XCUITest to validate core app functionalities achieving 95% test coverage.

• Implemented integration and delivery pipelines using Jenkins, reducing deployment time by 70% and improving overall code quality.

• Utilized RxSwift for managing concurrency and asynchronous actions.

• Collaborated on app design using Figma and adhered to iOS Human Interface Guidelines.

• Developed intuitive interfaces in Swift and UIKit for users to access digital ID cards, search for doctors, and track claims efficiently.

• Implemented user authentication and authorization protocols, including OAuth, to secure member data.

• Implemented data synchronization mechanisms to ensure consistency between local storage and cloud databases.