InfoBeans Technologies - Senior Quality Assurance Manager - Python Playwright

Role Overview

We are looking for an experienced Senior Manager QA / Quality Engineering to lead quality engineering transformation, automation strategy, and delivery across enterprise applications. The ideal candidate should have strong hands-on expertise in Python-based test automation using Playwright, API automation, CI/CD, and modern AI-driven quality engineering practices.

The role will involve building scalable automation frameworks, driving shift-left quality practices, establishing QA standards and KPIs, and leading distributed QA teams while partnering closely with Engineering, Product, and Business stakeholders.

Key Responsibilities

QA Leadership & Transformation :

• Lead QA and Quality Engineering strategy across multiple projects and products.
• Define and implement scalable QA processes, standards, methodologies, and quality KPIs.
• Drive Shift-Left testing and quality engineering practices across the SDLC.
• Establish automation-first approaches to improve test coverage, release velocity, and product quality.
• Build and evolve QA CoE capabilities, frameworks, reusable accelerators, and best practices.
• Identify opportunities for process optimization, test automation, and intelligent quality engineering.
• Drive root-cause analysis, defect prevention, risk assessment, and continuous improvement.
  
  

• Design, develop, and maintain UI automation frameworks using Python and Playwright.
• Establish scalable automation architecture using Page Object Model, reusable components, fixtures, and data-driven approaches.
• Develop automated functional, regression, smoke, and end-to-end test suites.
• Integrate Playwright automation with CI/CD pipelines using GitHub Actions, Jenkins, or Azure DevOps.
• Implement parallel execution, cross-browser testing, reporting, test retry, and flaky-test management.
• Evaluate and modernize existing Selenium-based automation frameworks by introducing Playwright and Python where appropriate.
• Drive automation coverage across critical business workflows and continuously improve automation ROI.
• Establish coding standards, framework guidelines, code reviews, and best practices for automation engineers.
  
  

• Lead API automation using tools such as Python, Postman, RestAssured, or Karate.
• Define API testing strategy covering functional, integration, negative, and contract testing.
• Drive performance testing using JMeter and other appropriate tools.
• Analyze performance bottlenecks and work with engineering teams on remediation.
• Integrate API and performance testing into CI/CD pipelines where appropriate.
  
  

• Explore and implement AI/GenAI-assisted testing to improve productivity and test effectiveness.
• Evaluate AI-assisted test generation, test optimization, defect triage, RCA assistance, and regression optimization.
• Define quality validation approaches for AI-powered applications, including :
• Response accuracy
• Contextual relevance
• Response consistency
• Hallucination detection
• Prompt effectiveness
• Edge-case handling
• Participate in designing and validating Agentic AI workflows and autonomous testing solutions.
• Identify opportunities to use tools such as Claude, Cursor, ChatGPT, MCP, and other AI technologies within QA workflows.
  
  

• Integrate automated testing into CI/CD pipelines.
• Establish quality gates and automated regression checks within deployment pipelines.
• Work closely with DevOps and Engineering teams to improve continuous testing and release confidence.
• Promote Git-based development, code reviews, test reporting, and engineering best practices.
  
  

• Lead, mentor, and develop QA/QE engineers across multiple projects.
• Build automation and quality engineering capabilities within the team.
• Conduct technical mentoring, framework reviews, and knowledge-sharing sessions.
• Define team goals, performance expectations, and development plans.
• Manage distributed teams and coordinate with global stakeholders.
  
  

• Partner with Product, Engineering, Business, and Program teams to define quality objectives.
• Own QA strategy, estimates, test plans, release readiness, and quality reporting.
• Communicate quality risks, dependencies, defects, and release recommendations to senior stakeholders.
• Support client discussions, solutioning, proposals, and QA transformation initiatives.
  
  

• 15+ years of experience in Software Testing / Quality Engineering, with significant experience in QA leadership.
• Strong hands-on experience in Python-based automation.
• Strong expertise in Playwright for web/UI automation.
• Experience designing and implementing automation frameworks from the ground up.
• Strong understanding of Selenium and modern automation framework architecture.
• Experience with API automation and testing.
• Strong experience with CI/CD tools such as Jenkins, GitHub Actions, or Azure DevOps.
• Experience with Git and modern software engineering practices.
• Strong understanding of Agile/Scrum, STLC, test strategy, and release management.
• Experience with performance testing, preferably JMeter.
• Experience leading and mentoring QA/QE teams.
• Strong stakeholder management and communication skills.
• Experience working with enterprise/SaaS applications.
  
  

• Experience in AI/GenAI testing and AI-assisted QA automation.
• Experience with Claude, Cursor, ChatGPT, LLMs, or MCP-based workflows.
• Knowledge of Agentic AI and autonomous workflow validation.
• Experience with AI-assisted test generation and intelligent test optimization.
• Experience with accessibility testing using tools such as NVDA and Axe.
• Experience with web performance tools such as Lighthouse/PageSpeed.
• Banking, Financial Services, Insurance, Payments, or other enterprise-domain experience.
• Experience establishing or scaling a QA CoE / Quality Engineering practice.
  
  

• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related discipline.
  
  

• Increased automation coverage and reduction in manual regression effort.
• Improved release quality and reduction in production defects.
• Adoption of Python + Playwright automation across strategic applications.
• Improved CI/CD and continuous testing maturity.
• Successful implementation of AI-assisted quality engineering initiatives.
• Strong team capability development and engineering productivity.
• Consistent quality governance, metrics, and stakeholder visibility.