QA Engineer

Job Summary

Required Qualifications

• •Detail-oriented
• •Organized
• •Analytical
• •Comfortable working across implementation, application engineering, data engineering, and AI-enabled platform delivery
• •Manual and automated validation capabilities
• •Regression testing experience
• •Exploratory testing experience
• •Implementation testing experience
• •User Acceptance Testing support experience
• •API testing experience
• •Data validation experience
• •AI accuracy checks experience
• •Load and performance testing experience
• •Quality controls for new platform capabilities and customer configurations
• •Develop and execute test plans for web applications, APIs, platform features, data workflows, AI-enabled capabilities, and customer-specific configurations
• •Strong analytical and problem-solving skills to isolate defects, identify reliable reproduction scenarios, and distinguish application defects from data, configuration, integration, or environment issues
• •Validate functional correctness, workflow behavior, business rules, data loads, integrations, permissions, user-facing outcomes, and platform configuration scenarios
• •Support User Acceptance Testing, implementation quality reviews, release validation, and production readiness for customer implementations
• •Document defects, reproduction steps, expected behavior, actual behavior, test results, risks, and validation evidence clearly and consistently
• •Work with implementation and engineering teams to confirm fixes, validate changes, and reduce recurring quality issues
• •Apply strong testing fundamentals while adapting validation practices to both custom software and low-code or hybrid platform delivery
• •Build, maintain, and execute automated regression tests for platform features, web applications, APIs, configuration scenarios, and customer implementation patterns
• •Identify application workflows, APIs, and platform capabilities where load or performance testing is appropriate, and support the development and execution of those tests
• •Partner with Application Engineering, Data & AI Engineering, Solution Engineering, and Platform Reliability to integrate automated tests into CI/CD and release processes
• •Identify repeatable validation needs and convert them into reusable automated test coverage where appropriate
• •Support test data preparation, environment readiness, smoke testing, regression testing, and release validation
• •Help maintain test suites that improve confidence across platform changes, configuration updates, API changes, and customer-specific implementations
• •Contribute to automation patterns that improve speed, consistency, and traceability of QA work
• •Capture failed extractions, edge cases, inconsistent outputs, prompt issues, unexpected AI behavior, and quality trends for review by Solutions Engineering and Data & AI Engineering
• •Support evaluation practices for AI accuracy, consistency, regression risk, and customer-specific acceptance criteria
• •Help ensure AI-enabled features are tested for reliability, traceability, explainability where appropriate, and operational readiness
• •Maintain appropriate human review, documentation, and validation evidence for AI-enabled workflows before production use
• •Test low-code, no-code, and hybrid platform configurations including workflows, business rules, forms, data loads, permissions, prompts, user journeys, and integrations
• •Support consistent quality practices across both custom engineering and configuration-led delivery
• •Validate that configured solutions connect correctly with data services, AI-enabled features, application workflows, APIs, authentication patterns, and customer-facing delivery processes
• •Validate APIs, data services, data loads, data transformations, extracted outputs, and integration behavior across platform workflows
• •Support testing of data-driven features, reporting outputs, structured review workflows, and customer-facing delivery processes
• •Verify that data used in applications, workflows, AI features, and customer deliverables is accurate, complete, and aligned with expected business rules
• •Partner with Data & AI Engineering to validate extraction workflows, data quality checks, AI-ready data outputs, and downstream platform behavior
• •Document data quality issues, mismatches, transformation errors, and integration defects clearly for engineering and implementation teams
• •Use AI-assisted methods where appropriate to support test case generation, exploratory testing ideas, documentation, and defect investigation
• •Contribute to reusable testing patterns, documentation, and validation approaches that improve QA team consistency and delivery speed
• •Support integration of automated tests into CI/CD pipelines and release validation processes
• •Support deployment readiness, regression testing, smoke testing, and production validation where appropriate
• •Communicate quality risks, test results, blockers, defects, and validation status clearly and consistently
• •Proactively identify ambiguous requirements, acceptance criteria, and unexpected behaviors, and work with Product, Engineering, and QA stakeholders to clarify expected outcomes before or during testing
• •Contribute to QA standards, test documentation, automation patterns, validation templates, and reusable quality practices
• •Participate in sprint planning, backlog refinement, defect triage, release planning, and retrospective discussions where appropriate
• •Contribute to a culture of accountability, collaboration, continuous improvement, customer focus, and delivery excellence
• •This role is based at our DNV office in Chennai, India