Rifki Rosada | Public Android + AI

I-ScanTea

Built an edge ML prototype for tea leaf quality scanning with a mobile-first inference flow.

ML Prototype Engineer 5-week prototype Public product build
  • Problem

    Manual quality checks required repeated visual comparison and lacked a fast digital workflow.

  • My scope

    Prepared the dataset and labeling workflow for the target classes.

  • Result

    Demonstrated an on-device classification workflow for field capture scenarios.

Problem

Manual quality checks required repeated visual comparison and lacked a fast digital workflow.

Constraints

  • Needed to run with limited connectivity
  • Inference had to stay lightweight for mobile hardware
  • Data quality varied across real capture conditions

Delivery

  • Prepared the dataset and labeling workflow for the target classes.
  • Built a compact inference pipeline suitable for mobile deployment.
  • Designed a capture and result flow for quick operator feedback.

Result

  • Delivered a usable prototype for offline-first quality checks.
  • Established a repeatable evaluation flow for future model updates.
  • Provided a base architecture for production hardening.

Tech stack

  • TensorFlow Lite
  • Android
  • Python
  • Edge ML

Related delivery proof across internal tools, automation, Android + AI, and supporting product systems.

Public Android + AI

Public build

OffScan AI

Android Product Engineer6-week build and release

Published an Android workflow that performs OCR locally without cloud upload requirements.

  • Problem

    Users needed OCR extraction with stronger privacy guarantees and no network dependency.

  • My scope

    Designed an offline-first OCR flow for camera capture and image import.

  • Result

    Published an Android workflow that performs OCR locally without cloud upload requirements.

  • Android
  • On-device OCR
  • Edge AI
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Public Android + AI

Public build

Scanberry

Mobile Computer Vision Engineer4-week prototype

Validated a practical on-device scan flow for classification without cloud dependency.

  • Problem

    The workflow needed rapid classification support in environments with unstable connectivity.

  • My scope

    Built a lightweight model iteration and evaluation loop.

  • Result

    Validated a practical on-device scan flow for classification without cloud dependency.

  • Computer Vision
  • Android
  • TensorFlow Lite
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Android + AI

Client delivery

Media Android App - AI Chat + Search UX

Android AI UX Engineer3-week delivery sprint

Made the AI-assisted discovery flow clearer and more reliable from chat prompt to search result.

  • Problem

    Users lacked a clear transition from AI chat prompts to meaningful search actions, and state sync issues reduced UX reliability.

  • My scope

    Worked inside the existing WebView-based AI entry path so the feature fit the live app architecture.

  • Result

    Made the AI-assisted discovery flow clearer and more reliable from chat prompt to search result.

  • Android
  • Kotlin
  • WebView
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