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Ayush Bhople

@abhople1902

Skill iconJavaScript
Node.js
Skill iconC++
Skill iconSwift
Shell

Nagpur, India

đź‘‹ Hello, I'm Ayush Bhople
I'm an iOS engineer who finds joy in solving real-world problems through elegant code and intuitive design. I thrive at the intersection of innovation and user experience.

🚀 What I'm Good At
Over the past few years, I’ve honed my skills across the full lifecycle of iOS app development—from ideation and architecture to seamless deployment and App Store optimization. I specialise in:

  • Swift & SwiftUI: Building fast, beautiful, and responsive apps with modern Apple technologies.
  • Offline-first Mobile Apps: Architecting robust solutions using MongoDB Realm and Realm Sync—especially for field work in environments with spotty connectivity.
  • Concurrency & Performance: Leveraging Swift Concurrency to make apps not just work, but fly.
  • Custom Swift Packages: Modularizing code for better reusability and maintainability across enterprise-scale projects.
  • SAP ERP Integration: Enabling real-time data flows in asset-intensive industrial applications.

đź’ˇ What Drives Me
I’m deeply motivated by impact. The idea that a well-crafted app can make someone’s day easier, a business more efficient, or a system more reliable is what fuels my passion. Whether it's healthcare, industrial inspections, or motorsports news—I dive into domains with curiosity and purpose.

đź”§ Projects I'm Proud Of
📱 mRounds – Field inspections, digitized
A mobile-first solution replacing manual operator rounds in industrial settings. Designed to be offline-resilient with Realm Sync, it ensures seamless field data capture even in remote environments.
View on App Store »

🏎️ Mode Race – F1 news on the go
Built with SwiftUI, this iOS app keeps Formula 1 enthusiasts updated with the latest news using the NewsData API. It’s fast, clean, and available on TestFlight.
Test it here »

🛰️ MLAIC: Aerial Image Classification
A research-driven computer vision project where I implemented VGG-19 to classify aerial images using deep feature extraction. This work led to a publication on IEEE Xplore.
Read the Paper »