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TextI am an AI engineering student with hands-on experience in data annotation, model evaluation, and training data generation for large language models. My work as a Data Annotator and AI Trainer has allowed me to develop expertise in applying complex annotation rubrics across diverse datasets, authoring and editing high-quality prompts and responses, and performing rigorous quality assurance to ensure data integrity. I am skilled in Python, familiar with frameworks like TensorFlow and Keras, and have practical experience with RLHF, adversarial testing, and bias mitigation. My background in healthcare and insurance has strengthened my attention to detail and ability to follow strict protocols—traits I bring to every data labeling project. I am passionate about leveraging my technical and analytical skills to contribute to robust, reliable AI systems.
Compare images, label specific features, describe visuals, spot differences, test prompts, add bounding boxes around particular things or people to determine a model's ability to seamlessly detect or change scenes.
Providing high quality speech data to help train and improve audio AI systems
Evaluating if the LLM can analyze how events in one modality trigger reactions in the other across the entire timeline, stump testing with hallucinations, and designing adversarial multimodal Q&A pairs that test long-context memory, cross-modal alignment, pattern recognition, ontology switching, temporal reasoning, and referential grounding.
Associate of Applied Science, Artificial Intelligence Software Engineering
Vocational Certification, Electrocardiogram Technology
Licensed Property and Casualty Agent
Dialysis Technician
