Product Quality Engineer II

Product & Quality Ownership

• Own end-to-end quality for assigned product areas, from requirements to release sign-off.
• Participate in requirement and design discussions; validate business workflows and customer use cases, including async and event-driven flows.
• Define comprehensive test strategies across UI, APIs, and event-based systems.
• Define and implement comprehensive test strategies covering functional, integration, regression, exploratory, negative, and risk-based testing.
• Ensure coverage for BVT, Sanity, Regression, and Exploratory testing with clear traceability to requirements

Automation & Frameworks

• Design, develop, and maintain scalable and reusable automation frameworks using Playwright with Python or JavaScript/TypeScript.
• Contribute to framework architecture to support UI, API, and event-driven automation.
• Build modular, maintainable test utilities and reusable components.
• Establish automation best practices and coding standards to enable reliable, low-flakiness automation adoption across teams.
• Implement parallel execution, tagging strategies, and optimized test suite organization.
• Improve automation coverage across critical business workflows and platform integrations.
• Continuously refactor and enhance framework stability, execution speed, and maintainability.

Tooling, AI & CI/CD:

• Test data creation
• Event mocking
• Environment orchestration
• Leverage AI-powered tools to accelerate:
• Test case generation
• Flaky test analysis
• Root cause identification
• Integrate UI, API, and event-based automation into Jenkins CI/CD pipelines.
• Continuously improve pipeline stability and reduce E2E test flakiness.

AI Skills & Competencies

AI-Augmented Testing

• Use AI/ML-based test generation tools (e.g., Testim, Diffblue, Cursor) to auto-generate and maintain test cases from requirements or code changes.
• Apply large language models (LLMs) and prompt engineering to generate edge case scenarios, test data, and exploratory test ideas.
• Use AI-driven analytics platforms to predict test failures, optimize test suite selection, and prioritize risk-based testing.

AI-Powered Quality Analysis

• Employ AI-based flaky test detection and self-healing frameworks (e.g., Healenium, Mabl) to reduce test maintenance overhead.
• Use AI root cause analysis tools to automatically triage failures, cluster similar defects, and recommend fixes.
• Apply natural language processing (NLP) to parse and validate unstructured requirement documents, release notes, and user stories for testability gaps.
• Leverage predictive analytics and ML models to identify high-risk code areas and prioritize regression coverage accordingly.

AI Tooling Familiarity (Preferred)

• Familiarity with Cursor, ChatGPT, or similar AI coding assistants for accelerating script writing, code reviews, and test authoring.
• Exposure to AI ethics and responsible testing practices — bias detection, fairness evaluation, and explainability of AI-driven product decisions.
  • Ability to evaluate and onboard emerging AI QA tools, benchmark their impact, and champion adoption across teams.