Senior Quality Assurance Engineer

Requirements

Key Responsibilities – Quality Engineering

• Understand business requirements, user stories, acceptance criteria, and product workflows to identify functional and non-functional testing requirements.
• Design, execute, and maintain effective functional, regression, integration, API, and end-to-end test scenarios.
• Identify critical business flows, risks, edge cases, negative scenarios, and boundary conditions.
• Develop and maintain robust UI automation using Playwright.
• Execute automated regression suites, analyze failures, and identify the root cause of failures rather than simply reporting failed tests.
• Identify, log, track, prioritize, and verify software defects throughout the development lifecycle.
• Collaborate closely with Developers, Product Managers/Product Owners, Business Analysts, and other QA engineers.
• Participate actively in requirement analysis, backlog refinement, sprint planning, story grooming, test planning, and release validation.
• Practice Shift-Left Quality Engineering by identifying defects and testability issues as early as possible in the development lifecycle.
• Review implementation-level changes where appropriate and perform deeper validation beyond black-box testing.
• Continuously improve test coverage, automation reliability, test execution efficiency, and overall product quality.

AI-Driven Quality Engineering

AI should be an integral part of the candidate's daily QA workflow rather than an occasional productivity tool. The candidate is expected to leverage AI across multiple phases of the Quality Engineering lifecycle, including:

Requirements & Test Planning

• Use AI to analyze requirements, user stories, acceptance criteria, and product documentation.
• Identify ambiguities, missing requirements, risks, dependencies, and potential gaps using AI.
• Generate and refine test scenarios using AI while applying appropriate QA judgment.
• Use AI to identify edge cases, negative scenarios, boundary conditions, and potential failure paths.
• Leverage AI to improve test coverage and risk-based test planning.

Test Case Design

• Generate, review, optimize, and maintain test cases using AI.
• Use AI to transform requirements and acceptance criteria into structured test scenarios.
• Identify redundant test cases and opportunities for regression-suite optimization.
• Generate test data and realistic test conditions using AI where appropriate.

Test Automation

• Use AI assistants to accelerate Playwright automation development, debugging, refactoring, and maintenance.
• Generate initial automation structures using AI and independently validate and improve the generated code.
• Use AI to analyze automation failures and suggest fixes.
• Explore and contribute to self-healing or AI-assisted automation capabilities where applicable.
• Build reusable AI-assisted workflows to improve automation productivity.

AI Skills & Agents

• Create and use AI Skills to automate recurring QA activities and standardize testing workflows.
• Develop or contribute to AI Agents that can perform QA-related tasks such as test analysis, test generation, defect analysis, regression analysis, or test execution support.
• Understand how to provide appropriate context, instructions, tools, and guardrails to AI agents.
• Explore integrations with tools such as MCP or other tool-based AI workflows to enable agents to interact with QA and engineering systems.
• Convert repetitive QA processes into reusable AI-powered workflows rather than performing them manually.

AI Evals & Quality of AI Outputs

• Understand the importance of Evals (evaluations) when building or using AI-powered QA workflows.
• Design basic evaluation criteria to measure the accuracy, consistency, usefulness, and reliability of AI-generated test cases, automation code, defect analysis, or other QA outputs.
• Create and maintain evaluation datasets/test cases for AI workflows where applicable.
• Analyze AI output quality and continuously improve prompts, Skills, Agents, workflows, and evaluation criteria.

Defect Analysis & Root Cause

• Use AI to assist with defect investigation, failure analysis, log analysis, and root cause analysis.
• Correlate test failures, application behavior, logs, and available technical information to accelerate defect investigation.
• Identify recurring defect patterns and potential quality risks using AI-assisted analysis.
• Where appropriate, contribute to AI-powered defect analysis and quality analytics solutions.

Required Skills & Experience

Quality Assurance

• Strong understanding of software testing fundamentals and SDLC/STLC.
• Strong experience in functional testing and test case design.
• Experience with regression, integration, and end-to-end testing.
• Strong understanding of defect lifecycle and root cause analysis.
• Good understanding of Agile/Scrum development practices.
• Strong analytical and problem-solving skills.

Test Automation

• Strong hands-on experience with Playwright.
• Ability to develop, maintain, debug, and optimize automation frameworks/scripts.
• Good programming/scripting skills, preferably JavaScript/TypeScript or Python.
• Experience with API testing and basic understanding of CI/CD is desirable.
• Understanding of automation best practices, maintainability, reliability, and test-data management.

AI & Modern Quality Engineering

The candidate should demonstrate practical hands-on use of AI in QA, not just theoretical awareness. Experience with one or more of the following is highly desirable:

• Claude / Claude Code
• AI-assisted test generation/test automation
• AI-assisted defect/root-cause analysis
• AI-based test data generation
• Claude Skills or equivalent reusable AI workflows
• AI Agents / Agentic QA workflows
• MCP or tool-based AI integrations
• AI Evals/evaluation frameworks
• Self-healing or AI-assisted automation
• AI-based test optimization or regression analysis

Candidates should be able to demonstrate how they have actually used AI to improve QA productivity, coverage, quality, or engineering outcomes.