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Text GenerationI have hands-on experience across two enterprise AI training projects spanning multiple data modalities. On the Aether Project with Outlier.ai, I labelled and annotated voice, video, image, and text datasets, performed RLHF tasks including rating and ranking AI-generated responses against structured rubrics, and provided written rationales to guide model reward functions. This work required consistent attention to annotation guidelines across high-volume task queues and a strong ability to evaluate AI outputs for accuracy, tone, and relevance. I am currently contributing to the Hedgehog Project via Handshake AI, where my scope expanded to include prompt writing, comprehensive AI response evaluation, and quality review. I produce multimodal training data across audio, video, image, and text pipelines, write prompts designed to surface edge-case model behaviours, and conduct quality checks to maintain inter-annotator consistency. Across both projects, I have worked on platforms including Outlier.ai, Scale AI, and Handshake.ai, developing a solid practical understanding of how high-quality human feedback drives model improvement in real-world AI training pipelines.
I have completed data labeling and AI training work on two projects — the Aether Project via Outlier.ai (voice, video, image, text annotation and RLHF) and the Hedgehog Project via Handshake AI (multimodal training data, prompt writing, AI response evaluation, and quality review). I am currently active on Hedgehog.
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AI Data Trainer — Hedgehog Project Handshake AI · Remote · 2024 – Present Producing multimodal training data across voi
