Mehmet Serhat Ozdursun
13+ Years in Quality Engineering
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Mehmet Serhat Ozdursun

Lead QA Automation Engineer | AI-Driven Quality Engineering | Mobile, API & CI/CD
ISTQB® CTAL-TM, CTAL-TAE, CTFL | Scrum.org PSD I
13+ Years in Quality Engineering

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GitHub
atsqa.org
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Email: serhat.ozdursun@gmail.com

Phone: +905368361407

Languages: Turkish (Native), English (C1), Spanish (B)

Lead QA Automation Engineer with 13+ years of experience delivering end-to-end quality engineering across mobile, web, and API platforms.

I specialize in building scalable automation frameworks, embedding quality across CI/CD pipelines, and defining automation strategy while staying hands-on.

Throughout my career, I have led QA initiatives across fintech, SaaS, and enterprise systems, increasing release confidence, reducing production defects, and improving delivery speed.

Recently, I have focused on AI-assisted testing, applying practical approaches to improve automation speed, reduce maintenance overhead, and support more scalable quality engineering practices.

I currently work in full-time contract roles as a Lead QA Automation Engineer, partnering with distributed teams to lead quality initiatives across large-scale mobile commerce platforms.

Engineering Projects

Real Engineering Work

Hands-on projects that demonstrate practical architecture, QA discipline, and engineering execution.

AI Visual Compare

AI-assisted UI comparison tool that detects visual differences between screenshots using OpenCV and LLM reasoning.

  • Python
  • OpenCV
  • Gemini
  • AI

AI-Assisted Mobile Test Automation with Maestro

Deterministic-first mobile test orchestration for scenario exploration, AI-assisted step generation, and on-device validation using Maestro.

  • Python
  • Maestro
  • Mobile Testing
  • AI
  • Test Automation

QA Engineering CI/CD Playground

The repository behind my personal website, used to demonstrate QA engineering best practices in CI/CD including GitHub Actions, unit and component tests, SonarQube, Danger-driven AI PR review, and AI-based QA-affected area analysis.

  • Next.js
  • TypeScript
  • GitHub Actions
  • SonarQube
  • DangerJS
  • AI

BDD Testing Frameworks

A unified BDD-style automation framework for API, web, and mobile testing. Although it is an older project, it reflects my early approach to building scalable and reusable test automation architectures across multiple layers.

  • Java
  • BDD
  • API Testing
  • Web Testing
  • Mobile Testing

Open Source Contributions

Ecosystem Contributions

Visible contributions and exploration across well-known test automation ecosystems.

WebdriverIO

Contribution and exploration related to IPC communication refactoring in the WebdriverIO test runner ecosystem.

  • TypeScript
  • WebdriverIO
  • Test Runner Architecture
View Repository

Articles & Knowledge Sharing

Thought Leadership in QA and AI

Practical writing focused on QA engineering, delivery risk, and AI-driven quality strategy.

What If AI Could Tell QA What Your Pull Request Might Break?

Explores how AI can analyze pull requests and help QA predict impacted test areas earlier in the delivery process.

  • AI in QA
  • Pull Request Impact Analysis
  • Test Coverage Awareness
Read Article

Self-Healing Locators That Report Themselves

Explores a smarter approach to UI test stability using self-healing locators combined with reporting mechanisms to reduce flaky test failures and improve reliability.

  • UI Test Automation
  • Self-Healing Locators
  • Test Stability
Read Article

What I'm Currently Exploring

  • Applying AI-assisted automation workflows for PR impact analysis and intelligent test selection.
  • Designing AI-assisted visual regression pipelines to detect meaningful UI risk with lower review overhead.
  • Building Maestro-based mobile automation flows and validating them on real devices for production-like confidence.
  • Designing an AI orchestration layer that converts natural-language test intent into executable, maintainable test flows.
  • Leveraging open-source contribution work in WebdriverIO to strengthen reusable automation patterns and execution reliability.

Professional Experience