Attractive Job Opening for Automation QA Engineer in Singapore

Job Description & Requirements

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