Role: Sr. QA Engineer/QA Lead (Modern Quality Engineering & AI Automation)
Experience Required: 7-12 years overall, with at least 2-3 years in a QA Lead or senior quality engineering role
We are looking for a strong Sr. QA Engineer/Lead to drive end-to-end quality engineering for enterprise applications and platform services. The role requires a hands-on leader who can define quality strategy, strengthen automation, improve release confidence, and drive modern QA practices across functional, API, integration, database, performance, and production validation. The ideal candidate should bring strong expertise in automation frameworks, API and backend validation, CI/CD integration, and the practical use of AI-assisted testing to improve productivity, coverage, and quality outcomes.
Role Summary
- Own QA strategy and quality governance across releases, including test planning, execution oversight, defect triage, sign-off, and risk management.
- Ability to use AI tools effectively for test design, test maintenance, defect analysis, and quality acceleration while applying strong human judgment and validation.
- Lead and strengthen automation across UI, API, integration, and regression testing using modern frameworks and AI-assisted approaches.
- Partner with Product, Engineering, DevOps, and stakeholders to improve release quality, reduce defect leakage, and increase delivery confidence.
- Drive modern quality engineering practices, including shift-left testing, CI/CD quality gates, reusable test assets, and continuous improvement.
Key Responsibilities
- Define end-to-end test strategy, test plans, coverage approach, entry/exit criteria, and release quality checkpoints for features and programs.
- Review requirements, architecture, workflows, and business scenarios to identify test coverage, risks, negative paths, and edge cases.
- Lead functional, integration, system, regression, exploratory, and production validation activities across enterprise applications and services.
- Drive API, microservices, and backend validation, including REST, GraphQL, service integration, data flow verification, and database validation.
- Own and enhance automation frameworks using tools such as Cypress, JMeter and API automation.
- Use AI-assisted testing capabilities to accelerate test design, improve test maintenance, identify high-risk areas, strengthen test data creation, and improve overall QA productivity, with human review and accountability.
- Integrate automated tests into CI/CD pipelines and establish quality gates for faster, reliable releases.
- Lead defect management, root cause analysis, triage, and quality reporting; track defect leakage, coverage, automation effectiveness, and release readiness.
- Ensure coverage for non-functional quality attributes such as performance, scalability, reliability, and security compliance as applicable.
- Mentor QA engineers, define standards and best practices, improve test discipline, and build stronger ownership across the team.
- Act as the primary QA point of contact for release planning, stakeholder communication, quality status, and risk escalation.
- Support SIT, UAT, production sanity, certification, and post-release quality validation activities.
Required Skills & Experience
- 7-12 years of experience in software quality assurance or quality engineering, with strong hands-on ownership in enterprise application testing.
- Strong experience in API, integration, microservices, UI, and database testing.
- Hands-on experience with automation tools and frameworks such as Cypress, JMeter, JavaScript/TypeScript, API testing etc.
- Strong understanding of CI/CD integration using tools such as Jenkins, GitHub, or similar platforms.
- Good knowledge of Agile/Scrum delivery, defect lifecycle management, quality metrics, and release governance.
- Hands-on experience in SQL/database validation and working knowledge of PostgreSQL.
- Practical experience with AI-assisted testing, including AI-supported test case generation, test maintenance, risk-based prioritization, or intelligent automation capabilities.
- Strong problem-solving, communication, stakeholder management, and mentoring skills.
- Experience in performance tuning, high-volume transaction systems, or large-scale enterprise workflows
Preferred Technical Exposure
- Enterprise applications built on Java, Node.js, microservices and service-oriented architectures.
- REST APIs, GraphQL, backend services, event-driven or integration-heavy systems.
- Database validation, data integrity checks, and workflow validation across environments.
- CI/CD, DevOps integration, release pipelines, and test execution in build workflows.
- Performance or scalability testing tools such as JMeter.
- Cloud and platform validation exposure, including AWS services, monitoring, or platform observability tools, is a plus.
AI and Modern Quality Expectations
- Ability to use AI tools effectively for test design, test maintenance, defect analysis, and quality acceleration while applying strong human judgment and validation.
- Awareness of modern intelligent testing concepts such as self-healing automation, risk-based prioritization, synthetic test data generation, and visual validation.
- Strong focus on reducing manual effort, improving automation resilience, and increasing release confidence through modern quality engineering practices.
- Ability to lead broader QA transformation initiatives and improve quality outcomes beyond project-level execution.
Education
Bachelor's degree in Computer Science, Information Technology, Engineering, or higher education
Nice to Have
- Exposure to compliance-driven environments and controls such as SOX, ITGC, ITAC, cybersecurity, or audit-sensitive systems.
- Experience with monitoring and observability platforms such as Datadog or equivalent.
Experience mentoring teams and driving QA capability uplift across multiple releases or products. |
