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 setting.
- 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 strong 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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