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Mid-level QA in a metro with generalist title but ML/LLM niche reduces applicant density.
QA skills transferable but ML/LLM output-validation requirement makes industry experience moderately important.
Explicit 5+ years requirement plus QA domain and ML/LLM validation preference raises filter strictness.
Own the technical testing of agent-generated outputs focusing on accuracy and turnaround time impact.
Build and maintain test suites validating outputs against golden-record datasets to support key performance indicators.
Conduct regression testing on agent and prompt changes to prevent accuracy drift in regulated processes and support human QA sign-off with technical evidence.
5+ years of QA/test engineering experience, preferably including ML/LLM output validation.
Experience defining test datasets and accuracy benchmarks for document or text-processing systems.
Domain process familiarity is a plus but not mandatory.
Work Experience Required: 5+ years
Experienced in validating and benchmarking outputs of machine learning or language model driven systems.
Comfortable working with regulated processes requiring accuracy and turnaround time adherence.
Capable of designing realistic test scenarios linking technical testing with business KPIs and human QA processes.