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Freelancer Overview

I am an experienced QA Engineer and AI Tester with over five years of hands-on experience in validating AI-driven products, including large language models (LLMs), retrieval-augmented generation (RAG) systems, and agentic AI workflows. My expertise lies in designing and executing comprehensive test plans and data validation strategies to ensure the accuracy, reliability, and fairness of AI outputs, with a strong focus on detecting bias, hallucinations, and edge-case failures. I have worked closely with cross-functional teams to build and own QA processes from the ground up, leveraging tools like Promptfoo for AI evaluation, as well as Cypress, Selenium, and Postman for automation and API testing. My background includes extensive data quality validation for AI inference pipelines and natural language search features, ensuring high-quality training and evaluation data for NLP applications. I am adept at both manual and automated testing, using industry-standard platforms and frameworks, and have a proven track record of improving data annotation workflows and reducing defects in fast-paced, remote-first environments. My commitment to rigorous testing and data integrity has contributed to the successful deployment of scalable, user-trusted AI systems across multiple organizations.

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