Karyna N.

About Karyna N.

I’m a Senior QA Engineer with deep experience testing complex systems across web, mobile, AR/VR, and streaming platforms. Over the years, I’ve contributed to large-scale projects for companies like Meta (Ray-Ban Stories), Paramount+, Infinity Reality, and others — focusing on manual testing, regression cycles, UAT, API validation, and compliance (GDPR, COPPA, etc.).My work blends a strong eye for UI/UX accuracy, detailed bug documentation, and end-to-end testing of Stripe payment flows, domain provisioning, and Google Marketplace integrations. I’m fluent in tools like Postman, Charles Proxy, Playwright, Qase.io, and Jira, ensuring high-quality releases across multi-platform environments.Recently, I’ve expanded into AI and LLM quality assurance, developing automated model validation frameworks and safety pipelines to ensure reliability, fairness, and security in intelligent systems. My goal is to bridge traditional QA precision with the new challenges of AI/ML quality, safety, and trustworthiness.Led Generative AI and Large Language Model (LLM) evaluation, focusing on accuracy, reliability, and real-world performance.Conducted LLM output validation and quality assurance, identifying hallucinations, bias, inconsistency, and failure modes. Performed AI output reliability analysis, ensuring response consistency across varied inputs and edge cases.Executed AI usability and UX evaluations to improve clarity, relevance, and user trust in AI-driven experiences. Defined, applied, and monitored AI quality metrics to measure performance, accuracy, and compliance.Evaluated multilingual AI outputs, validating linguistic accuracy, cultural relevance, and tone consistency. Designed and implemented AI response ranking and scoring frameworks to benchmark model performance.Conducted end-to-end quality analysis of AI-powered applications, covering functional, usability, and risk assessments. Ensured UI standards compliance across AI-integrated interfaces and user workflows. Applied SDLC quality analysis to support shift-left testing, risk mitigation, and continuous improvement.

Experience

1. Senior Qa Engineer

   Napster Corp.

   Nov 2024 — Present

   Led Generative AI and Large Language Model (LLM) evaluation, focusing on accuracy, reliability, and real-world performance- Conducted LLM output validation and quality assurance, identifying hallucinations, bias, inconsistency, and failure modes- Performed AI output reliability analysis, ensuring response consistency across varied inputs and edge cases- Executed AI usability and UX evaluations to improve clarity, relevance, and user trust in AI-driven experiences- Defined, applied, and monitored AI quality metrics to measure performance, accuracy, and compliance- Evaluated multilingual AI outputs, validating linguistic accuracy, cultural relevance, and tone consistency- Designed and implemented AI response ranking and scoring frameworks to benchmark model performance- Conducted end-to-end quality analysis of AI-powered applications, covering functional, usability, and risk assessments- Ensured UI standards compliance across AI-integrated interfaces and user workflows- Applied SDLC quality analysis to support shift-left testing, risk mitigation, and continuous improvement.

Education

• Stanford Continuing Studies

  Python, Computer Engineering

  2020 — 2020

• Stanford Continuing Studies

  Data Analysis with Python

• Kharkov Institute of Finance of Ukrainian State University of Finance and International Trade

  Accounting, Accounting and Business/Management

  2010 — 2014