Job Description

The QA Engineer is responsible for validating customer implementations, platform features, web applications, configuration scenarios, APIs, data-driven workflows, and AI-enabled capabilities across Digital & Transformation. This role sits within the Solutions Engineering section as part of a QA team that supports quality across implementation, engineering, and platform delivery.

This role sits within Digital & Transformation, helping to advance how DNV performs Due Diligence, Verification & Assurance, and Renewables Certification work across Energy Systems.

Working in close partnership with Solutions Engineering, Data & AI Engineering, Application Engineering, Product leadership, Platform Reliability, and Solution Architecture, this role helps ensure that web applications, APIs, low-code and hybrid platform configurations, AI outputs, and customer-specific workflows meet functional, quality, security, and production readiness expectations.

The QA Engineer supports both manual and automated validation, including regression testing, exploratory testing, implementation testing, User Acceptance Testing support, API testing, data validation, AI accuracy checks, load and performance testing, and quality controls for new platform capabilities and customer configurations.

This role plays a key part in improving delivery quality, reducing regression risk, strengthening release confidence, and ensuring new capabilities are reliable before they reach production environments. The ideal candidate is detail-oriented, organized, analytical, and comfortable working across implementation, application engineering, data engineering, and AI-enabled platform delivery.

**This role is based at our DNV office in Chennai, India. Further details regarding role-specific requirements will be shared during the interview process.**

Key Responsibilities

Quality Assurance & Test Delivery

• Develop and execute test plans for web applications, APIs, platform features, data workflows, AI-enabled capabilities, and customer-specific configurations.
• Apply 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.

Automation & Regression Testing

• 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.

AI Quality & Validation

• 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.

Low-Code, Hybrid Platform & Application Testing

• 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.

Data, API & Integration Testing

• 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.

Modern Development & AI-Enabled Testing

• 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.

DevOps, Reliability & Security

• 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.

Collaboration & Continuous Improvement

• 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.

• Flexible work arrangements for better work-life balance
• Generous Paid Leaves (Annual, Sick, Compassionate, Local Public, Marriage, Maternity, Paternity, Medical leave)
• Medical benefits ( Insurance and Annual Health Check-up)
• Pension and Insurance Policies (Group Term Life Insurance, Group Personal Accident Insurance, Travel Insurance)
• Training and Development Assistance (Training Sponsorship, On-The-Job Training, Training Programme)
• Additional Benefits (Long Service Awards, Mobile Phone Reimbursement)
• Company bonus/Profit share.

*Benefits may vary based on position, tenure/contract/grade level*

• Bachelor's degree in Computer Science, Information Systems, Engineering, or related field, or equivalent experience.
• 2–3 years of experience in software quality assurance, test automation, software testing, implementation validation, platform validation, or related roles.
• Experience with exploratory testing, defect investigation, and developing clear and reproducible defect scenarios.
• Familiarity with low-code, no-code, or hybrid development environments.
• Experience building, maintaining, or executing automated regression tests.
• Experience with test case design, defect management, test planning, release validation, and User Acceptance Testing support.
• Ability to validate functional correctness, data-driven workflows, and AI-enabled outputs with strong attention to detail.
• Familiarity with CI/CD processes, source control, test automation practices, and modern software delivery methods.
• Strong analytical, documentation, communication, and problem-solving skills.
• Ability to work through structured backlogs, delivery plans, task tracking, or equivalent delivery methods.

What is Preferred

• Experience supporting interoperability across multiple systems or enterprise platforms.
• Experience with load, performance, or scalability testing of web applications or APIs.
• Experience with collaborative due diligence, document review, workflow, reporting, Q&A, risk management, or transaction delivery applications.
• Background in energy, infrastructure, renewables, assurance, certification, or other technically complex industries.
• Experience working with globally distributed engineering, implementation, or QA teams..

Security and compliance with statutory requirements in the countries in which we operate is essential for DNV. Background checks will be conducted on all final candidates as part of the offer process, in accordance with applicable country-specific laws and practices.

We help customers navigate the complex transition to a decarbonized and more sustainable energy future. We do this by assuring that energy systems work safely and effectively, using solutions that are increasingly digital. We also help industries and governments to navigate the many complex, interrelated transitions taking place globally and regionally, in the energy industry.