Full house Annotation
Executed precise data and speech labeling workflows to annotate complex full house datasets, directly enhancing the accuracy and performance of AI and Machine Learning training models by using image labelling and bounding-box.
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I have over a two year of hands-on experience in high-volume data annotation and labeling, working with multimodal datasets including text, image, audio, and video to support advanced AI and machine learning model development. My expertise includes using tools like the SiriGradingPlugin for projects such as object recognition, speech labeling, image boxing, and audio transcription—directly contributing to the improvement of systems like Siri and automated content filtering. I am skilled at executing precise data validation, identifying mismatches, and collaborating with QA and engineering teams to refine annotation guidelines and ensure high-quality, reliable training data. My strong analytical abilities, attention to detail, and commitment to workflow efficiency enable me to deliver consistently accurate results in fast-paced AI data environments.
Executed precise data and speech labeling workflows to annotate complex full house datasets, directly enhancing the accuracy and performance of AI and Machine Learning training models by using image labelling and bounding-box.
This project evaluates the accuracy of image-text alignment by verifying semantic consistency, assessing visual quality for technical artifacts, and implementing grid-based structural improvements to enhance clarity and professional hierarchy
• Executed precise data and speech labeling workflows to annotate complex time-series datasets, directly enhancing the accuracy and performance of AI and Machine Learning training models. • Analyzed time-series patterns to detect trends and anomalies, meticulously tagging specific audio events and speech features to improve model recognition capabilities.
Bachelor of Technology, Mechanical Engineering
Data Annotator