Mugada S.p.k.bhargav

Key Skills

Software

Labelbox

Prodigy

Scale AI

SuperAnnotate

Roboflow

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Top Task Types

Bounding Box

Classification

Entity Ner Classification

Segmentation

Freelancer Overview

I have hands-on experience in AI data labeling and AI training tasks, primarily focused on text-based datasets used for training and evaluating large language models (LLMs). My work involves manual verification, annotation, and classification of structured and unstructured text data, following strict quality and accuracy guidelines. I have contributed to projects such as company profiling and legal entity verification, where I verified business attributes including company names, jurisdictions, websites, revenue ranges, directors, and payment methods using open-source intelligence (OSINT) like official registries, corporate websites, LinkedIn, and trusted public databases. I ensured high data accuracy by cross-checking multiple sources and documenting verified values clearly in spreadsheet-based workflows. Additionally, I have experience in text classification, entity recognition (NER-style tagging), prompt-response evaluation, and AI output quality review for LLM training. I follow human-only annotation standards, apply logical reasoning, and maintain consistency and attention to detail to support high-quality AI model training.

Labeling Experience

YOLOv8 Object Detection – Image Annotation Project

RoboflowImageBounding BoxClassification

I worked on annotating an image dataset for an object detection model using YOLOv8. The project involved drawing accurate bounding boxes around objects such as people, vehicles, and everyday items. I performed detailed image-level classification, ensured proper label consistency, and followed strict annotation guidelines. The dataset size included several hundred images, and each annotation was double-checked for accuracy. I also reviewed model predictions and provided corrections and quality feedback to improve training performance. My focus throughout the project remained on maintaining high precision, consistency, and adherence to the project’s QC standards.

I worked on annotating an image dataset for an object detection model using YOLOv8. The project involved drawing accurate bounding boxes around objects such as people, vehicles, and everyday items. I performed detailed image-level classification, ensured proper label consistency, and followed strict annotation guidelines. The dataset size included several hundred images, and each annotation was double-checked for accuracy. I also reviewed model predictions and provided corrections and quality feedback to improve training performance. My focus throughout the project remained on maintaining high precision, consistency, and adherence to the project’s QC standards.

2024 - 2024

Education

Work History