Evaluate and enhance AI-generated text, image, and multimodal output for accuracy, coherence, and compliance with complex project guidelines.
Identify hallucinations, factual inaccuracy, or cultural bias in LLM responses, and provide fairness and context validity.
Apply complex rubrics and rating scales to provide consistency and reliability in datasets.
Provide concise, actionable feedback to peers and assist in driving continuous quality-standard iteration.
Collaborate with Project Managers and QA Leads in iterating evaluation criteria, prompt engineering, and data-quality pipelines.
Active participation in Discord and cross-team channels for the resolution of ambiguous cases and developing guideline consistency.
Registered a 98%+ reviewer accuracy rating on numerous large-scale Generative AI projects.
Received linguistic comprehension and excellence in moral judgment awards from Outlier AI and TELUS project managers.
Supported various fine-tuning model initiatives that trended toward safer and context-aware AI systems.
Analytically driven and detail-oriented English Language Specialist with more than seven years of experience in AI testing, linguistic quality assurance, and academic research. Skilled at analyzing and enhancing multimodal AI outputs—including text, image captions, and prompts—to ensure accuracy, coherence, and cultural appropriateness.
I have worked with Outlier AI, Remotasks (Scale AI), TELUS International, and DataForce (TransPerfect) on large LLM and Generative AI projects, developing prompt analysis, annotation, and fine-tuning testing. These collaborations enhanced my expertise in human-in-the-loop quality control, bias detection, and ethical AI assessment.
Merging an English and Information Systems background with practical experience in AI model testing, I merge sharp attention to linguistic nuance, contextual accuracy, and guideline precision to help shape reliable, responsible AI systems.
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