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Quality Assurance Automation Engineer - Manufacturing Systems and Infrastructure
AppleBengaluru, Karnataka, India5+ years
Apply Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other's ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It's the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you'll do more than join something you'll add something.
Manufacturing Systems and Infrastructure (MSI) team is an engineering organisation under the Product Operations org. MSI is responsible for the design, development and maintenance of system tools, services and applications required to efficiently run manufacturing operations at scale across global factory sites.
As a Quality Assurance Automation Engineer with the MSI team, you will leverage Large Language Models and AI-native testing tools to rapidly build, scale, and maintain automated test suites across web, mobile, and API interfaces. You will design intelligent automation frameworks, generate synthetic datasets for edge-case and load testing, and embed smart execution strategies into CI/CD pipelines - all while championing GenAI adoption and best practices across the QA organisation.
- Leverage LLMs and AI coding assistants (e.g., GitHub Copilot, Cursor, ChatGPT) to rapidly generate, refactor, and document automated test scripts for web, mobile, and API interfaces.
- Design, build, and maintain scalable test automation frameworks, integrating AI-native testing tools (e.g., Applitools for visual AI, Testim/Mabl for self-healing locators).
- Utilise Generative AI to design and generate massive, diverse, and secure synthetic datasets for complex edge-case testing and load testing.
- Integrate automated suites into CI/CD pipelines (GitHub Actions, Jenkins) and implement "smart execution" strategies (using ML/AI to determine exactly which tests need to run based on the code commit).
- Utilise AI tools to rapidly parse server logs, stack traces, and crash reports to identify root causes of test failures, categorising bugs automatically.
- Partner with product and engineering teams to define test strategies, ensuring maximum test coverage while reducing maintenance overhead through intelligent automation.
- Champion the use of AI within the QA team, creating prompt libraries, guidelines, and best practices for using GenAI to write test cases and acceptance criteria.
- 5+ years of experience in QA Automation, System Development in Test (SDET), or a similar engineering role
- Expertise in Python, C/C++, Objective-C, and Swift, leveraging modern AI-assisted (vibe coding) development workflows
- Deep experience applying AI-powered techniques to generate, optimise, and maintain automated test suites with modern testing frameworks such as Playwright, Cypress, Selenium, Appium, or XCTest
- Demonstrated ability to write effective prompts to extract test scenarios, edge cases, and automation code from Large Language Models
- Familiarity with AI-powered testing tools (e.g., Applitools Eyes, ReportPortal's ML auto-analyzer, or self-healing UI tools)
- Solid understanding of CI/CD pipelines, Docker, and version control (Git)
- Ability and willingness to travel up to 30% (domestic and international)
- Bachelors / Masters in Computer Science or related fields
- Experience designing and generating synthetic datasets for large-scale load and edge-case testing
- Hands-on experience implementing ML/AI-driven smart test-selection or "smart execution" strategies in CI/CD
- Experience building and curating prompt libraries or GenAI usage guidelines for a QA or engineering organisation
- Excellent written and verbal communication skills, with the ability to partner effectively across product and engineering teams
- Self-motivated with a strong ownership mindset and a passion for continuously improving automation coverage and reliability
- Strong problem-solving skills with a focus on reducing "flaky tests" through resilient coding and AI insights
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