Software Engineer In Test

What You’ll Do:

• Contribute to product quality, reliability, and performance through effective risk-based testing.
• Define test strategies and design manual and automated tests across UI, REST/GraphQL APIs, database, and end-to-end workflows.
• Develop, maintain, and improve test automation frameworks using Python, Playwright, pytest, and Robot Framework.
• Integrate and optimize automated tests within CI/CD pipelines (e.g., Jenkins) to improve coverage and release confidence.
• Validate REST/GraphQL APIs and database data flows with attention to data integrity and business impact; investigate logs, failures, and defects.
• Apply AI-assisted engineering tools to support test design, edge-case discovery, automation development, and debugging, while reviewing generated outputs.
• Where applicable, contribute to test coverage for AI/LLM-enabled features, including response quality, tool interactions, and regression behavior.
• Collaborate with developers, product managers, and DevOps to identify quality risks and continuously improve QA practices.

Who You Are:

• Bachelor’s degree in Computer Science, Information Technology, or a related engineering discipline, or equivalent qualification.
• 1+ years of hands-on experience in SDET or QA automation, including test framework development or maintenance.
• Strong programming and scripting fundamentals; Python is preferred.
• Solid understanding of functional, regression, integration, and end-to-end testing methodologies.
• Hands-on experience with Playwright for UI automation, pytest and/or Robot Framework for test execution, and SQL-based data validation (MySQL preferred).
• Ability to integrate automated tests with CI/CD pipelines, analyze failures, and track test coverage and execution health.
• Strong problem-solving, troubleshooting, debugging, and communication skills; comfortable in an Agile engineering environment.
• Experience working in Linux/Unix environments and a basic understanding of operating systems.
• Understanding of performance testing concepts; exposure to JMeter or similar tools is an advantage.
• Practical familiarity with AI coding assistants (e.g., Cursor, GitHub Copilot, Claude, or similar) for test planning, automation, code review, and failure analysis is a plus.
• Understanding of Generative AI and LLM concepts, prompt engineering, model behaviour, hallucinations, and structured outputs is a plus.
• Exposure to testing AI/LLM applications, including RAG and agentic workflows; familiarity with response quality, grounding, retrieval relevance, and regression evaluation is a plus.
• Awareness of Model Context Protocol (MCP), function/tool calling, and AI evaluation techniques such as golden datasets or LLM-as-judge is a plus.
• Familiarity with LangChain, LangGraph, and AI observability tools and practices (e.g., LangSmith or Langfuse) for tracing and evaluating agent behaviour is a plus.