We are seeking an experienced and forward-thinking Automation QA Lead with strong expertise in Agentic AI and Test Automation to lead our quality engineering initiatives. The candidate must understand how to leverage Agentic AI to derive advanced testing strategies, optimize test planning, improve automation efficiency, and enhance overall software quality.

Agentic AI is a key requirement for this position. Candidates should understand how AI agents can support intelligent testing strategies, test design, automation optimization, and quality engineering workflows. The role also requires the ability to troubleshoot existing automation frameworks and improve testing efficiency across API, web, and mobile applications.

Key Responsibilities

1. Agentic AI & Individual Contribution 50%

- Agentic AI for Testing: Leverage Agentic AI to derive advanced testing strategies, identify testing gaps, optimize test planning, and improve quality assurance processes.
- AI-Driven Test Strategy: Apply AI-agent-based approaches to analyze application behavior, assess risks, identify critical test scenarios, and improve test coverage.
- Automation Framework Development: Design, develop, maintain, and optimize scalable, reusable automation frameworks using Selenium, Playwright, or Cypress.
- Hands-on Script Development: Write robust and maintainable automation scripts using Java, Python, JavaScript, or TypeScript.
- AI-Assisted Automation: Work with AI evaluation and coding tools such as KaneAI and GitHub Copilot to improve test authoring and automation workflows.
- KaneAI Integration: Use KaneAI to create, plan, and evolve test cases using natural language and integrate them into existing automation workflows.
- Web and Mobile Automation: Automate web applications and native or hybrid mobile applications across Android and iOS.
- API Automation: Develop and maintain API automation suites using REST Assured, Postman, and Newman.
- CI/CD Integration: Integrate automated tests into Azure DevOps CI/CD pipelines to enable continuous testing.
- Cloud-Based Testing: Use LambdaTest or similar platforms for cross-browser and cross-device testing.




- Automation Troubleshooting: Diagnose and fix existing automation framework issues, flaky tests, inefficient scripts, and failures in integrated systems.
- Complex Test Scenarios: Solve automation challenges involving OTP-based authentication, reCAPTCHA handling, API integrations, and complex end-to-end workflows.
- Test Execution and Reporting: Analyze execution results, investigate failures, report defects with clear reproduction steps, and improve regression and sanity testing reliability.
- Technical Collaboration: Work with developers and product stakeholders to define test coverage, acceptance criteria, and release readiness.

1. Team Management & Leadership 50%

- Team Leadership: Lead, mentor, and develop QA automation engineers, encouraging the adoption of Agentic AI-driven testing strategies and modern automation practices.
- AI Testing Adoption: Guide the team in applying AI-assisted testing methodologies and integrating suitable AI-driven approaches into QA workflows.
- Sprint and Resource Management: Manage sprint planning, task allocation, resource utilization, and delivery timelines.
- Quality Strategy: Establish standards for automation design, test coverage, framework maintainability, and test reliability.
- Performance Monitoring: Define and track KPIs related to automation coverage, script reusability, execution stability, defect detection, and delivery efficiency.
- Continuous Improvement: Identify opportunities to improve existing automation processes, reduce repetitive manual effort, and increase testing productivity.

Key Performance Indicators (KPIs)

- Agentic AI Adoption: Effectively apply Agentic AI to derive testing strategies and improve test planning and coverage.
- Coverage Optimization:



Identify gaps and expand test automation across API, web, and mobile applications.
- Framework Efficiency: Improve framework scalability, maintainability, and script reusability.
- Test Reliability: Reduce flaky tests and ensure stable, repeatable sanity and regression testing.
- Testing Productivity: Improve testing efficiency through optimized automation workflows and appropriate AI-assisted approaches.
- Team Delivery: Maintain quality standards and achieve agreed testing milestones through effective team management.

Skills and Qualifications

- Bachelor's degree in Computer Science, Information Technology, or a related field.
- 6+ years of experience in QA automation across API, web, and mobile applications.
- Robust practical understanding of Agentic AI and its application in software testing, test strategy development, test planning, and QA process optimization.
- Knowledge of AI agents and AI-assisted approaches for identifying test scenarios, optimizing test coverage, and improving automation workflows.
- Experience building scalable and reusable automation frameworks from scratch.
- Proven ability to lead, mentor, or manage QA automation engineers.
- Strong programming skills in Java, Python, JavaScript, or TypeScript.
- Hands-on experience with Selenium, Playwright, or Cypress.
- Experience with KaneAI or comparable AI-powered testing tools is highly relevant.
- Experience with API automation using REST Assured, Postman, or Newman.
- Strong understanding of HTTP, JSON, OAuth, JWT, and API authentication.
- Experience with Azure DevOps and CI/CD pipeline integration.
- Familiarity with LambdaTest or similar cloud-based testing platforms.
- Experience automating native and hybrid mobile applications on Android and iOS.
- Strong database knowledge for test data validation and database testing.
- Ability to troubleshoot and optimize existing automation setups and resolve complex testing challenges.
- Strong analytical, debugging, problem-solving, and communication skills.
- Excellent verbal and written English communication skills.

Immediate Joiners preferred.

📌 Automation QA Lead ( Agentic AI and Test Automation) (Bengaluru)
🏢 Intellicredence
📍 Bengaluru