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Tier-1 employer, mid-level QA title, metro location, and popular role increase applicant competition.
QA fundamentals are transferable but AI model validation specialization increases industry-specific bias.
Explicit 5+ years QA, 2+ years AI focus, plus mandatory Python and automation skills increase filter strictness.
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Lead AI model validation strategies including accuracy testing, regression testing, and performance evaluation across hardware and deployment environments.
Develop, maintain, and execute test plans and automated testing frameworks for AI features and modeling capabilities, covering functional, integration, performance, and regression testing.
Drive automation initiatives and mentor QA team members while collaborating cross-functionally to ensure quality, reliability, and delivery of AI-powered software releases.
Bachelor’s degree in mechanical, computer science, data science, AI, or related field.
5+ years of software QA experience, including 2+ years specifically focused on AI or ML applications.
Proficiency in Python for test automation and strong understanding of AI/ML concepts including model training, inference, and validation.
Experience with AI model validation techniques addressing data drift, non-deterministic outputs, edge case testing, and output reliability.
Experienced in testing AI-driven systems requiring qualitative, statistical, and scenario-based validation approaches rather than deterministic testing.
Able to lead initiatives and collaborate effectively in Agile/Scrum environments, contributing technical leadership for AI-focused QA.
Familiarity with AI testing frameworks, quality metrics for AI outputs, and integrating validation within CI/CD pipelines for AI-enabled development.