About the role
AI summarisedThe Quality Analyst (AI Platform) role at Apple focuses on ensuring the quality, reliability, and correctness of AI-powered and LLM-based features in production environments. The position involves designing and executing test strategies, validating data across complex hierarchies and systems, collaborating with product and engineering teams, and driving data quality monitoring and release decisions. This role blends traditional QA with AI-specific validation techniques in a fast-paced, cross-functional setting.
TechnologyOnsiteMachine Learning and AI
Key Responsibilities
- Define and implement the test strategy based on Functional Requirements driven by the Product Manager
- Work closely with the Product Managers to define test plans that ensure data quality both on new development and regular data loads
- Drive test coverage across different source systems, transactional applications and reporting environments, taking into account new features and regression
- Execute test scenarios in a repeatable manner, allowing for easy data quality monitoring
- Coordinate release management and hold the final go / no-go in terms of data quality and functional behaviour
- Act as first gate for Production Ops issues, confirm the behavior and makes the call on priority with Product Manager
- Report findings in a clear, structured, and actionable manner
- Collaborate with Engineers to understand implementation logic
- Manage tickets on found data issues and work with the Product Manager to plan out bug fixes
- Communicating status updates to end users and stakeholders concisely in a timely manner
Requirements
- Strong communicator with the ability to interpret technical concepts and data findings for non-technical end users
- Experience applying a data-driven QA approach, including data reconciliation or validation, in business-critical environments
- Hands-on experience with SQL and at least one analytics or reporting tool (e.g., Business Objects, Tableau, or similar), with the ability to independently investigate and validate datasets
- Hands-on experience designing, implementing, and executing automated test cases using languages or frameworks such as Python, JavaScript, or Selenium
- Experience validating backend APIs, data pipelines, or service-based systems using automated approaches
- Experience working in cross-functional teams using Agile frameworks such as Scrum or Kanban
- Familiarity with LLM-based or AI-powered systems, including hands-on experimentation or professional exposure
- Understanding of prompt-based systems and how changes to prompts or inputs affect outputs
- Experience using collaboration and tracking tools such as Jira, Confluence, Quip, or similar
- Strong problem-solving skills with attention to detail
- Ability to learn quickly and adapt in a fast-paced environment
- Professional experience testing LLM-based systems (e.g., chat, summarization, classification, extraction, or RAG workflows)
- Experience building or maintaining scalable automated test frameworks beyond test case execution
- Strong proficiency in SQL, including complex joins, aggregations, and large dataset validation
- Understanding of Dimensional Modeling and Data Warehousing concepts
- Experience testing data migration projects, including source-to-target validation
- Familiarity with CI/CD pipelines and integrating automated tests into release workflows