Senior Quality Assurance Manager

InfoBeans Inc.; Pune District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

InfoBeans Inc. in Pune is seeking a Senior Manager – QA / Quality Engineering with 15+ years of software testing leadership. You will drive automation strategy, AI‑driven QA practices, and shift‑left quality across enterprise applications.

You will build scalable automation frameworks, govern QA standards, and mentor distributed QA teams while collaborating with Engineering, Product, and Business stakeholders.

Qualifications

• 15+ years of experience in Software Testing / Quality Engineering with leadership experience.
• Strong hands-on experience in Python-based automation.
• Proven expertise with Playwright for web/UI automation and automation framework design.
• Experience defining scalable QA processes, standards, and KPIs.

Responsibilities

• 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.

Skills

Python automation

Playwright

API automation

CI/CD tooling

QA strategy & governance

Mentoring & leadership

Selenium

Performance testing

Education

Bachelor's or Master's degree in Computer Science / IT / Engineering

Tools

GitHub Actions

Jenkins

Azure DevOps

JMeter

Job description

Senior Manager – QA / Quality Engineering

Location: Pune

Experience: 15+ years

Role: Senior Manager – QA / Quality Engineering

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

• 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.

Test Automation – Python & Playwright

• 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.
• 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.

API & Performance Testing

• 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.

AI-Driven Quality Engineering

• 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
• 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.

Stakeholder Management

• 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.

Required Skills & Experience

• 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.

Preferred / Good-to-Have Skills

• 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.

Education

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

Key Success Measures

• 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.