Attractive Job Opening for Automation QA Engineer in Singapore

ABOUT THIS FEATURED OPPORTUNITY The QA Engineer will join the Channel Sales andOperations team to help ensure the reliability and quality of our AI/ML-poweredB2B chatbot and the foundational platforms supporting it. This role goes beyondtesting outputs you'll be working closely with engineers to ensure theend-to-end system, including cloud infrastructure and data pipelines, functionsas intended. THE OPPORTUNITY FOR YOU Design and execute manual and automated test cases for GenAI platforms and chatbot systems.

Build and maintain Python-based test automation frameworks for backend services and ML pipeline validation. Utilize RAGAS or similar tools to assess LLM outputs for factuality, relevance, and system performance. Conduct end-to-end testing from data ingestion to user-facing output. Validate system stability across GCP cloud components compute, storage, networking, and containers. Identify failures not only in chatbot answers, but also in underlying infrastructure and platform behavior.

Collaborate with DevOps and ML engineers to triage bugs and optimize performance. Ensure test coverage spans across multiple deployment environments including Kubernetes clusters and cloud VMs. Requirements KEY SUCCESS FACTORS 3+ years of QA Automation Engineering experience Experience with Playwright for automated application testing, including sign-in and SSO authentication flows Ability to design tests that validate output accuracy and system behavior across different user flows Experience with Python API testing using requests, Pytest, and integration into CI/CD and cloud environments Proficiency in writing and debugging Bash scripts used in CI/CD and cloud deployment workflows Experience with Cloud platforms ( GCP preferred), including Kubernetes (kubectl experience is a plus), virtual machines, databases, cloud networking and storage components Understanding of modern cloud architecture and how distributed components interconnect in production environments NICE TO HAVES Experience with LLM testing frameworks like RAGAS , and ability to interpret metrics such as factuality, relevance, and performance Experience with monitoring and observability tools Familiarity with the end-to-end architecture of GenAI solutions , including vector stores, retrievers, embedding models, and inference systems