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Mid-level QA is common, metro location and general automation skills increase competition despite GenAI specialization.
Role requires GenAI evaluation and ETL data-quality expertise, creating strong domain-specific hiring preference.
Multiple explicit mandatory requirements (5+ QA, 4+ ETL, 2+ AI, Python automation) indicate strict filtering.
Develop and maintain Python-based automated test suites for Generative AI features including Retrieval-Augmented Generation and conversational AI.
Design and execute end-to-end test strategies covering functional, safety, data quality, and non-functional aspects such as performance and cost-efficiency of GenAI systems.
Collaborate with AI engineers, data engineers, and product teams to define acceptance criteria and validate AI model evaluation, ETL pipeline data quality, and API contract adherence.
5+ years experience in software QA including test strategy, automation, and defect management.
2+ years experience testing AI/ML or Generative AI features with hands-on evaluation design.
4+ years experience testing ETL/data pipelines and validating data quality.
Strong Python skills for automated testing (e.g., PyTest, requests) and experience with REST API testing and GenAI evaluation techniques.
Experienced in designing and implementing risk-based test plans for complex AI systems integrating data pipelines and advanced language models.
Familiar with GenAI-specific evaluation metrics such as factuality, hallucination detection, safety, bias, and prompt-injection resilience.
Skilled at collaborating cross-functionally with AI, data engineering, and product teams to translate quality objectives into measurable test acceptance criteria and automated validations.