QA Lead

We are looking for a QA Lead - Automation and AI Testing who can take end-to-end ownership of product quality while leading and mentoring a QA team.

You will work closely with Product, Engineering, and other stakeholders from requirements through release, ensuring that testing is practical, risk-based, and aligned with business priorities. This role requires someone who is comfortable being hands-on with testing and automation while also taking ownership of planning, quality metrics, release readiness, and team development. The ideal candidate should have strong experience in automation testing, Playwright or similar tools, API testing, performance testing, and AI feature validation, along with at least one year of experience leading a QA team.

The core responsibilities for the job include the following:

QA Team Leadership and Mentoring:

• Lead, guide, and coach the QA team to improve technical and testing capabilities.
• Plan sprint QA activities, distribute tasks, and track progress.
• Identify and remove testing-related blockers.
• Conduct regular 1:1s and provide clear, actionable feedback to team members.
• Support team members in improving their testing practices, automation skills, and overall ownership.
• Establish a culture of quality, accountability, and continuous improvement.

End-to-End Quality Ownership:

• Own the quality of assigned modules/features from requirements and planning through production release.
• Participate in requirement discussions, refinement sessions, sprint planning, and design reviews.
• Identify quality risks early and ensure appropriate test coverage.
• Provide a data-driven Go/No-Go recommendation for releases based on test results, defect status, risk, and business impact.
• Ensure production releases meet defined quality standards.

Test Strategy and Planning:

• Develop simple, practical, and risk-based test strategies for products and features.
• Prioritize testing based on business impact, technical complexity, and risk.
• Define what needs to be tested manually, automatically, regression-tested, exploratory-tested, deferred, or skipped based on risk.
• Maintain appropriate test plans, scenarios, cases, and regression suites.

Stakeholder Management:

• Conduct and participate in regular QA and cross-functional meetings with Product, Engineering, and other stakeholders.
• Lead discussions around sprint QA planning, testing status, defect status, release readiness, and quality risks.
• Communicate testing progress and risks clearly to technical and non-technical stakeholders.
• Escalate critical issues early and ensure timely resolution.

Defect Management and Root Cause Analysis:

• Review, prioritize, and track defects based on severity, customer impact, and business priority.
• Ensure critical and high-impact bugs are addressed before release.
• Monitor defect trends and recurring issues.
• Conduct Root Cause Analysis (RCA) for escaped defects and production issues.
• Identify process or testing gaps and implement corrective actions to prevent recurrence.

QA Metrics and Reporting:

• Define, monitor, and improve QA KPIs such as defect leakage, defect density, automation coverage, requirement-to-test coverage, regression pass rate, test execution progress, flaky test rate, bug fix turnaround time, production defect rate, and test case effectiveness.
• Use these metrics to provide clear visibility into product quality and release readiness.

AI-Assisted Testing:

• Actively use AI tools such as ChatGPT, Claude, GitHub Copilot, or similar tools across the QA lifecycle.
• Use AI for drafting and reviewing test cases, generating test scenarios, creating test data, identifying test coverage gaps, generating edge cases, summarizing defects and test results, reviewing API responses, supporting automation development, and analyzing test failures.
• Guide the QA team to validate and review AI-generated output before relying on it.
• Identify opportunities to improve QA productivity through responsible AI adoption.

Hands-on Testing and Automation:

• Remain hands-on and actively contribute to testing and automation activities, including functional testing, regression testing, exploratory testing, integration testing, and API testing using Postman or similar tools Performance testing using JMeter or similar tools; UI automation using Playwright or similar frameworks; test automation framework development and maintenance; automated regression suites; and CI/CD test integration.
• Balance leadership responsibilities with hands-on technical contribution.

AI Feature and Chatbot Testing:

• Test AI-powered features, including chatbots and AI-driven workflows.
• Evaluate AI responses for: Relevance, Accuracy, Completeness, Tone, Context Awareness, Consistency, and Safety.
• Create test scenarios for ambiguous, unexpected, adversarial, and out-of-scope questions.
• Validate that AI features respond appropriately when they do not have sufficient information.
• Identify hallucinations, incorrect responses, prompt-handling issues, and unexpected behaviors.
• Build and maintain a practical AI testing approach for continuous validation.

Requirements:

• 3+ years of overall QA/testing experience with strong hands-on automation experience.
• 2+ years of experience in automation testing.
• 1+ year of experience leading or mentoring a QA team.
• Strong hands-on experience with Playwright or similar automation frameworks.
• Experience with Jira or similar defect/project management tools.
• Good understanding of functional, regression, exploratory, integration, and API testing.
• Experience with Postman or similar API testing tools.
• Experience with JMeter or similar performance testing tools.
• Strong understanding of test planning, test strategy, defect management, and release processes.
• Experience defining and tracking QA metrics.
• Good understanding of CI/CD and automated testing integration.
• Experience testing AI/ML-powered features or chatbots is highly desirable.
• Hands-on experience with ChatGPT, Claude, GitHub Copilot, or similar AI tools for QA productivity.
• Strong analytical, problem-solving, communication, and decision-making skills.
• Ability to work independently and take complete ownership of product quality.

Good to Have:

• Experience working in a SaaS/product-based environment.
• Experience building automation frameworks from scratch.
• Experience with Git and CI/CD tools.
• Experience with cloud-based applications.
• Knowledge of performance, security, or reliability testing.
• Experience defining QA processes for a growing engineering team.
• Experience testing LLM-based applications, RAG systems, AI agents, or conversational AI.