: QA Automation Lead

Experience Required: 10–15 Years

Location: Indore / Pune / Bangalore

Role: QA Automation Architect / SDET Transformation Lead – AI-Enabled Quality Engineering

This is a transformation role with a strong technical foundation . The person must be able to inspect frameworks, question architecture choices, challenge release practices, push for better testability, and translate QA execution into measurable assurance.

What This Person Will Own

1. Technical QA Architecture

- Design and govern scalable automation frameworks across UI, API, database, and integration testing.
- Provide hands-on leadership in Python, Java, Playwright, Selenium, pytest, Rest Assured, API automation, and CI/CD-integrated execution .
- Review framework design, coding standards, test data strategy, reporting, maintainability, and automation health.
- Partner with architects to evaluate trade-offs, delivery constraints, release risk, and quality impact.
- Challenge weak engineering practices when they create downstream QA risk.
- Protect QA standards when delivery pressure increases.

1. QA Operating Model

- Standardize QA processes, artifacts, tooling, defect workflows, release-readiness reporting, and test governance across programs.
- Define metrics that demonstrate:
- Requirement coverage
- Automation health
- Defect patterns
- Triage speed
- Release risk
- Escaped defects
- Regression depth
- Readiness to ship
- Build reusable playbooks for sprint QA, regression, automation review, release validation, test data readiness, defect triage, and production-readiness checkpoints.
- Create the first version of missing artifacts when programs lack structure, such as release notes, test summaries, coverage reports, or readiness dashboards.

1. AI-Enabled QA Transformation

- Translate recurring QA work into repeatable workflows that can be supported by AI agents,



prompt templates, and skill files .
- Identify QA tasks suitable for agent-assisted execution, including:
- Test-case generation
- Coverage mapping
- Failure triage
- Locator repair
- Regression selection
- Test data preparation
- Release-note drafting
- Dashboard reporting
- Train QA teams to orchestrate AI-supported QA workflows while preserving human review, governance, accountability, and auditability.
- Partner with InfoBeans RAI teams to bring workflow realities into product and accelerator evolution.
- Define what humans own, what agents can draft, what requires review, and what must remain under release governance.

1. Talent Development and Team Leadership

- Assess current QA capability across manual QA, automation QA, senior SDETs, and QA leads.
- Build a practical upskilling roadmap for approximately 30 QA professionals over two years .
- Coach team members on test design, automation thinking, tool discipline, client communication, release ownership, and QA accountability.
- Create a robust second line of QA leads who can manage day-to-day execution with less escalation.
- Bring inconsistent teams to one shared QA standard.

1. Client and Stakeholder Leadership

- Act as InfoBeans’ QA counterpart to architects, tech directors, project leads, and central QA stakeholders.
- Communicate clearly across engineering, QA, business, and leadership audiences.
- Surface risks early with options, impact, and recommended actions.




- Push teams toward the work required to release safely.
- Build confidence through evidence, metrics, and follow-through.

Required Experience
- 10–16+ years of experience in SDET, QA Automation, or Quality Engineering.
- Strong hands-on automation background with Python, Playwright, Selenium, pytest, Rest Assured, TypeScript , or equivalent tools.
- Proven experience designing or materially improving automation frameworks for complex enterprise applications.
- Deep experience with API automation, CI/CD pipelines, test reporting, defect workflows, test data strategy, and release validation .
- Experience with Jira, Xray, Zephyr, TestRail, Allure, Azure DevOps, Jenkins, GitHub Actions , or similar tooling.
- Experience defining QA strategy, quality metrics, test governance, release-readiness practices, and automation standards.
- Ability to lead or influence 15–30 QA professionals across distributed teams.
- Solid stakeholder presence with architects, engineering leads, QA leadership, and client-side governance teams.
- Experience in BFSI, banking, payments, capital markets, insurance, or regulated enterprise environments is preferred.

Strong Positives
- Prior experience in banking, capital markets, payments, risk, controls, financial systems, or regulated enterprise delivery.
- Experience partnering with a central QA team or enterprise testing CoE.
- Experience creating quality dashboards, release-readiness reports, governance artifacts, and metric packs.
- Experience moving manual QA teams toward automation and higher-value orchestration.
- Exposure to AI-assisted testing, self-healing automation, test generation, smart test selection, MCP, LLM workflows, RAG validation, or prompt engineering .
- Ability to write or review code while continuing to lead and mentor teams.

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