Job Summary
- 15+ years of software quality engineering experience with a bachelor’s or master’s degree in Computer Science, Software Engineering, or a related technical discipline
- Extensive hands-on experience defining and executing test strategies, test plans, test cases, and quality gates across large-scale enterprise large scale applications
- Experience in solution architecture for QA, SDLC transformation, and embedding quality practices within Agile and DevSecOps delivery models
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Generative AI & Intelligent Test Automation (2+ Years)
- Hands-on experience building AI-powered test automation using GenAI frameworks: LangChain, LangGraph, CrewAI, AutoGen, or MCP Protocol
- Strong knowledge of LLM APIs (Anthropic Claude, OpenAI GPT-4o) for generating test cases, test data, and defect analysis summaries
- Experience using Retrieval-Augmented Generation (RAG) to query requirements and policy documents to auto-generate business-aligned test scenarios
- Proficiency in prompt engineering techniques (Chain-of-Thought, few-shot strategies) to produce high-quality test artefacts via LLMs
- Exposure to cloud AI platforms for QA: AWS Bedrock, Azure OpenAI Service, or Google Vertex AI
Automation Testing Tools & Frameworks
- Extensive hands-on experience with UI and API automation frameworks: Selenium WebDriver, Playwright, Cypress, or Appium for web and mobile applications
- API and service-layer testing: REST Assured, Postman/Newman, Karate DSL, or SOAP UI for functional and contract testing
- Performance and load testing: Apache JMeter, Gatling, or AWS Distributed Load Testing for stress, soak, and scalability validation
- BDD frameworks: Cucumber (Gherkin), SpecFlow, or Behave — bridging business requirements and automated test execution
- Security testing: OWASP ZAP, Burp Suite, or SonarQube for vulnerability scanning and static code analysis integrated into CI/CD pipelines
- Test management platforms: Jira/Xray, ALM/Micro Focus, TestRail, or Azure Test Plans for traceability and defect lifecycle management
- DevOps integration: Jenkins, GitHub Actions, or GitLab CI for continuous testing pipelines; Docker and Kubernetes for containerized test execution
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Test Strategy, Architecture & SDLC Transformation
- Demonstrated ability to define and own enterprise-wide test strategies, test architectures, and quality frameworks aligned to programme objectives
- Experience authoring test plans, entry/exit criteria, traceability matrices (RTM), and test summary reports for regulatory and audit purposes
- Proven ability to transform manual QA processes into shift-left, AI-assisted, and continuous-testing models within Agile/SAFe delivery
- Strong experience in risk-based testing, defect triage, root-cause analysis, and quality metrics reporting to senior stakeholders
- Ability to establish Test Centres of Excellence (TCoE), define testing standards, and govern quality across multiple workstreams
Key Responsibilities
Generative AI & Intelligent Test Automation (2+ Years) Hands-on experience building AI-powered test automation using GenAI frameworks: LangChain, LangGraph, CrewAI, AutoGen, or MCP Protocol Strong knowledge of LLM APIs (Anthropic Claude, OpenAI GPT-4o) for generating test cases, test data, and defect analysis summaries Experience using Retrieval-Augmented Generation (RAG) to query requirements and policy documents to auto-generate business-aligned test scenarios Proficiency in prompt engineering techniques (Chain-of-Thought, few-shot strategies) to produce high-quality test artefacts via LLMs Exposure to cloud AI platforms for QA: AWS Bedrock, Azure OpenAI Service, or Google Vertex AI Automation Testing Tools & Frameworks Extensive hands-on experience with UI and API automation frameworks: Selenium WebDriver, Playwright, Cypress, or Appium for web and mobile applications API and service-layer testing: REST Assured, Postman/Newman, Karate DSL, or SOAP UI for functional and contract testing Performance and load testing: Apache JMeter, Gatling, or AWS Distributed Load Testing for stress, soak, and scalability validation BDD frameworks: Cucumber (Gherkin), SpecFlow, or Behave — bridging business requirements and automated test execution Security testing: OWASP ZAP, Burp Suite, or SonarQube for vulnerability scanning and static code analysis integrated into CI/CD pipelines Test management platforms: Jira/Xray, ALM/Micro Focus, TestRail, or Azure Test Plans for traceability and defect lifecycle management DevOps integration: Jenkins, GitHub Actions, or GitLab CI for continuous testing pipelines; Docker and Kubernetes for containerized test execution Test Strategy, Architecture & SDLC Transformation Demonstrated ability to define and own enterprise-wide test strategies, test architectures, and quality frameworks aligned to programme objectives Experience authoring test plans, entry/exit criteria, traceability matrices (RTM), and test summary reports for regulatory and audit purposes Proven ability to transform manual QA processes into shift-left, AI-assisted, and continuous-testing models within Agile/SAFe delivery Strong experience in risk-based testing, defect triage, root-cause analysis, and quality metrics reporting to senior stakeholders Ability to establish Test Centres of Excellence (TCoE), define testing standards, and govern quality across multiple workstreams Insurance Domain Experience Hands-on testing experience with core insurance platforms: ALIP (Accenture Life Insurance Platform), Oracle Insurance Policy Administration (OIPA), or equivalent policy management systems Experience testing end-to-end insurance processes: policy issuance, underwriting workflows, claims processing, billing and premium calculation, and policy servicing Familiarity with insurance product types: life, annuities, group benefits, and health insurance — including complex policy rules and regulatory validation Knowledge of insurance data standards and regulatory compliance testing: SOX, NAIC, state-level insurance regulations, and GDPR-equivalent data-privacy requirements Experience validating system integrations between policy admin, CRM, billing, reinsurance, and reporting systems in an insurance ecosystem Soft Skills & Professional Competencies Excellent written communication skills — ability to produce clear test strategies, architecture documents, and executive quality dashboards for both technical and business audiences Strong verba
Skill Requirements
Soft Skills & Professional Competencies
- Excellent written communication skills — ability to produce clear test strategies, architecture documents, and executive quality dashboards for both technical and business audiences
- Strong verbal communication — able to articulate testing risks, quality posture, and AI-driven QA initiatives confidently in steering committees and programme reviews
- Stakeholder engagement — proven ability to work closely with business analysts, developers, product owners, and compliance teams to define and uphold quality standards
- Collaborative team player — comfortable leading and working within distributed QA teams while also being self-driven in individual analysis and test design
- Mentoring & knowledge sharing — willingness to coach QA engineers, promote automation best practices, and build test engineering capability across teams
- Analytical thinking — structured problem-solver who can identify root causes, assess testing risks, and propose pragmatic quality solutions under delivery pressure
- Adaptability — thrives in fast-moving environments where AI technologies, insurance regulations, and business priorities evolve rapidly
Other Requirements
1. Microsoft Certified: Azure Solutions Architect Expert (Recommended)