Key Skills

Software

AWS SageMaker

Data Annotation Tech

Label Studio

OpenCV AI Kit (OAK)

Other

Top Subject Matter

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Top Data Types

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Top Task Types

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Freelancer Overview

Having worked on data labeling and AI training data, I’ve worked on structuring and annotating various kind of datasets (text, images, and audio) for machine learning models such as classification, bounding box annotation, semantic segmentation, and NLP labeling (NER, sentiment analysis, intent detection). High-quality, consistent labels is critical, as poor data directly impacts model performance. I’ve employed standard practices inter-annotator agreement checks, iterative refinement of labeling guidelines, and using tools like Label Studio, CVAT, or Prodigy to streamline workflows. Also, I’ve collaborated with ML engineers to preprocess and balance datasets, addressing biases and edge cases. For generative AI, I’ve worked with LLM fine-tuning data (prompt-response pairs, RLHF, and synthetic data generation). A key lesson is that clean, well-documented training data is as important as the model architecture itself—without it, even advanced algorithms is liable to under-deliver.

Labeling Experience

Education

Work History