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Specialized AI-testing reduces applicant pool, but QA title and broad automation skills sustain moderate competition.
Automation and API testing are transferable, but LLM/RAG-specific skills raise domain sensitivity to medium.
Explicit 8–10 years and numerous mandatory AI, automation, and tooling skills increase shortlisting rigidity.
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Lead and enhance quality engineering for AI-powered platforms, focusing on AI system accuracy, reliability, fairness, and safety.
Own the design and implementation of testing strategies and automation frameworks for GenAI applications, LLM evaluation, and RAG pipelines.
Drive shift-left quality practices and integrate automated testing into CI/CD pipelines across Agile teams.
8 to 10 years of quality engineering experience with proven expertise in AI/GenAI testing and test automation.
Hands-on experience with LLM-powered applications and RAG pipeline testing including hallucination detection and prompt regression.
Proficiency with test automation tools like Selenium or Playwright, API testing (REST, GraphQL, gRPC), and advanced Python scripting.
Experience integrating quality gates and automated test suites into CI/CD pipelines (e.g., Jenkins, GitLab CI, GitHub Actions, Azure DevOps).
Experienced in both traditional test automation and specialized AI/GenAI system validation, including responsible AI evaluation and adversarial testing.
Strong background in building scalable automation frameworks and designing test strategies aligned with risk-based quality goals.
Ability to collaborate with data scientists, ML engineers, and stakeholders to ensure production-grade AI system quality and compliance.