About the Company

About the Role
Senior AI-Native Quality Engineer Responsibilities 1. AI-Native Quality Engineering
• AI is a core requirement of this role. You will integrate AI into the entire quality lifecycle—not simply use it as an occasional chatbot.
• Analyse requirements and acceptance criteria to identify gaps, edge cases, ambiguities and potential risks.
• Use AI to generate and enhance test scenarios, automation scripts and synthetic test data.
• Leverage AI coding assistants to accelerate development, refactoring, debugging and test maintenance.
• Use AI to analyse failed tests, logs, screenshots and stack traces to accelerate root-cause analysis.
• Explore AI-assisted test maintenance, intelligent regression test selection and self-healing automation.
• Automate test reporting, result analysis and repetitive quality-engineering activities wherever practical. 2. Automation & Continuous Testing
• Design, develop and maintain scalable automation frameworks using Playwright and relevant supporting technologies.
• Build robust API automation and validation, prioritising API-level testing where appropriate to reduce unnecessary UI testing.
• Develop and maintain functional, integration, regression and end-to-end automated tests.
• Use Appium for mobile automation where required; Selenium experience is an added advantage.
• Reduce flaky tests, enable parallel execution and improve test reliability and feedback cycles.
• Integrate automated tests into Jenkins or equivalent CI/CD pipelines and establish meaningful, risk-based quality gates.
• Continuously identify manual and repetitive testing activities that can be automated, simplified or eliminated. 3. AI / LLM Quality Engineering
• Validate AI-powered features for accuracy, relevance, consistency, reliability, context awareness and failure behaviour.
• Test for hallucinations, prompt injection, adversarial inputs, unsafe outputs, guardrail failures and boundary conditions.
• Define evaluation criteria, test datasets and automated evaluation pipelines for AI-powered applications.
• Understand and validate the complete Retrieval-Augmented Generation (RAG) workflow:



Query → Retrieval → Context → LLM → Response.
• Evaluate retrieval quality, context relevance, response grounding and failure scenarios in RAG-based applications.
• Test AI agents and tool/function-calling workflows for incorrect tool selection, invalid parameters, repeated calls, state-management issues and failure recovery.
• Understand the challenges of testing non-deterministic AI systems and apply suitable evaluation techniques.
• Strongly preferred: Hands-on experience with RAG, embeddings, vector databases, conversational AI, AI agents or tool/function calling. 4. Quality Engineering & Product Ownership
• Take ownership of product quality throughout the development lifecycle rather than focusing solely on test execution.
• Challenge unclear requirements, assumptions and implementation decisions constructively.
• Collaborate closely with developers, product managers and other stakeholders to identify and mitigate quality risks early.
• Contribute to requirement refinement, sprint planning, design discussions, release planning and retrospectives.
• Investigate defects, perform root-cause analysis and assess their impact from the customer's perspective.
• Make informed, risk-based decisions that balance release speed with product quality in a fast-moving Agile environment.
• Communicate quality risks clearly and confidently, including challenging release decisions when critical issues remain unresolved. Required Skills 5. Required Technical Skills
• 5–8 years of experience in software testing, SDET or Quality Engineering, with solid hands-on automation experience.
• Strong programming skills in Java, TypeScript or JavaScript.
• Strong hands-on experience with Playwright and automation framework design.




• Solid API automation experience using REST Assured or equivalent technologies.
• Experience with CI/CD pipelines, Git, debugging and software-testing fundamentals.
• Strong understanding of functional, integration, regression and end-to-end testing.
• Practical experience designing maintainable, reliable and scalable automated test suites.
• Appium or mobile automation experience is strongly preferred.
• Experience with Selenium, TestNG/JUnit, SQL, Docker, cloud-based test execution, performance testing or security testing is an added advantage. 6. Mandatory AI Skills
• Practical experience using AI in software engineering, test automation or Quality Engineering.
• Hands-on use of AI-assisted test generation and automation development.
• Experience applying AI to debugging, failure analysis, root-cause investigation and test maintenance.
• Understanding of prompt engineering, LLM behaviour, evaluation methods and AI-specific failure modes.
• Hands-on experience testing LLM-powered applications, including hallucination, prompt-injection and boundary-condition testing.
• Strongly preferred: Experience with OpenAI, Azure OpenAI, AWS Bedrock or Gemini, along with RAG applications, vector databases, embeddings, AI agents, tool/function calling or automated LLM evaluation. Preferred Skills 7. What We Look For
• Engineering mindset: Thinks like an engineer who owns quality, not simply a test executor.
• AI-first approach: Uses AI as an everyday engineering productivity multiplier and evaluates its output critically.
• Automation ownership: Automates where it adds measurable value and actively eliminates repetitive QA work.
• Critical thinking: Challenges assumptions, identifies risks early and asks the right questions.
• Strong communication: Communicates confidently with developers and product stakeholders and can constructively challenge decisions.
• Problem-solving: Takes ownership of failures and explains why they occurred, their impact and how recurrence can be prevented.
• Continuous learning: Adapts quickly and stays curious about emerging AI technologies and modern Quality Engineering practices.

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