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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 .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.