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Mid-level role, metro location and common QA title increase competition, offset by AI-specialization.
Specialized AI/ML QA and LLM evaluation skills reduce cross-industry transferability.
Explicit 3–6 years, required AI/ML QA experience, Python and tooling requirements increase shortlisting strictness.
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Design and execute test strategies and automated frameworks specifically for AI/ML models, LLM applications, and data pipelines to validate performance and reliability.
Evaluate AI model outputs for accuracy, bias, hallucination, and degradation including monitoring models in production for drift and anomalous behavior.
Collaborate with data scientists and ML engineers to define quality criteria, build evaluation datasets, and ensure AI systems meet fairness and ethical standards.
Bachelor's or Master's degree in Computer Science, Engineering, or related field.
3-6 years of QA experience with at least 1-2 years in AI/ML quality assurance.
Strong proficiency in Python and experience with testing tools such as Pytest, Selenium, and Postman.
Familiarity with LLM evaluation frameworks and understanding of ML lifecycle including data quality and pipeline testing.
Experienced QA professional focused on AI/ML products with deep understanding of AI-specific failure modes like hallucinations, bias, and model drift.
Operationally skilled in automated testing, model evaluation, and production monitoring in complex ML/AI environments.
Collaborative worker familiar with cross-functional engagement involving data science, engineering, and product teams to enforce quality and ethical standards.