Attractive Job Opening for Automation QA Engineer in Singapore

1 week ago

Singapore IITMATRIX PTE. LTD. Full time $90,000 - $120,000 per year

ABOUT THIS FEATURED OPPORTUNITY

The QA Engineer will join the Channel Sales and Operations team to help ensure the reliability and quality of our AI/ML-powered B2B chatbot and the foundational platforms supporting it. This role goes beyond testing outputs you'll be working closely with engineers to ensure the end-to-end system, including cloud infrastructure and data pipelines, functions as 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

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