This is a hands-on, quality-focused role responsible for ensuring the reliability, performance, security, and scalability of enterprise-grade applications and AI-driven solutions.
The incumbent will work closely with product, engineering, data, and AI teams to define quality strategies, validate complex systems, and drive test automation across modern application architectures including cloud-native, data-intensive, and AI/ML-enabled platforms.
You will contribute to end-to-end quality assurance across functional, non-functional, and automated testing, while helping establish QA best practices, frameworks, and quality governance in fast-paced delivery environments.
Key Responsibilities
Quality Strategy & Stakeholder Engagement
- Collaborate with product owners, architects, developers, and data/AI teams to define test strategies aligned with business and technical requirements.
- Participate in requirement reviews, design discussions, sprint planning, and release readiness assessments.
- Act as a quality advocate, ensuring risk-based testing and early defect detection across the SDLC.
- Communicate testing progress, risks, and quality metrics to technical and non-technical stakeholders.
Test Design & Execution
- Design, develop, and execute comprehensive test plans, test cases, and test scenarios for enterprise-level applications.
- Perform functional, integration, system, regression, UAT support, and exploratory testing.
- Validate complex workflows involving APIs, microservices, data pipelines, and third-party integrations.
- Ensure data accuracy, integrity, and consistency across systems.
Automation & Tooling
- Design and maintain test automation frameworks for UI, API, and backend testing.
- Implement automated tests using modern tools and integrate them into CI/CD pipelines.
- Promote automation-first and shift-left testing practices.
- Review and optimize automated test suites for reliability, maintainability, and coverage.
AI & Advanced Testing
- Validate AI/ML-based features including model outputs, recommendations, predictions, and decision logic.
- Test AI systems for accuracy, bias, explainability, robustness, and edge cases.
- Collaborate with data science teams to understand datasets, model behavior, and evaluation metrics.
- Support testing of data-driven applications, analytics dashboards, and AI-powered workflows.
Non-Functional Testing
- Conduct or coordinate performance, load, stress, and scalability testing.
- Validate security requirements including authentication, authorization, data privacy, and compliance.
- Ensure application reliability, observability, and production readiness.
Quality Governance & Continuous Improvement
- Define and enforce QA standards, processes, and best practices.
- Track defects, analyze root causes, and drive continuous quality improvements.
- Contribute to test metrics, coverage analysis, and quality reporting.
- Support release management and post-production validation.
Team & Knowledge Leadership
- Contribute to reusable test assets, automation accelerators, and documentation.
- Represent QA in cross-functional discussions and delivery forums.
