Suryansh S.

Key Skills

Software

CVAT

Other

Top Subject Matter

Computer vision (autonomous driving, retail automation, medical imaging)

dataset QA and preparation.

Natural language processing (chatbot training, LLM fine-tuning, text classification)

Top Data Types

Image

Text

Top Task Types

Segmentation

Entity (NER) Classification

Freelancer Overview

Freelance Data Annotation & Machine Learning Specialist (Global Freelance Platforms: Upwork, Fiverr, remote clients). Core strengths include CVAT and Other. Education includes Bachelor of Science, DPD (2022). AI-training focus includes data types such as Image and Text and labeling workflows including Segmentation and Entity (NER) Classification.

Labeling Experience

Freelance Data Annotation & Machine Learning Specialist (NLP annotation for chatbots/LLM projects)

OtherTextEntity (NER) Classification

Provided NLP data annotation services for text classification and conversational tagging. Produced labeled datasets for sentiment analysis, intent tagging, and named entity tracking (NER) to support chatbot training and LLM fine-tuning. Ensured quality through auditing and correction of labeling inconsistencies prior to delivery. • Annotated text for classification, sentiment analysis, intent/conversational tagging, and NER entity tracking. • Validated entity spans and label consistency using QA checks. • Supported client training workflows by preparing datasets suitable for model fine-tuning. • Reviewed and corrected edge cases to reduce training-time errors.

2022 - Present

Freelance Data Annotation & Machine Learning Specialist (Global Freelance Platforms: Upwork, Fiverr, remote clients)

CVATImageSegmentation

Served as a freelance Data Annotation & Machine Learning Specialist preparing production-ready datasets for computer vision tasks. Performed image and video annotation including bounding boxes, polygons, and semantic segmentation for domains such as autonomous driving, retail automation, and medical imaging. Maintained consistent annotation quality through structured QA and auditing practices to support downstream model training and optimization. • Annotated high-resolution images and videos using bounding boxes, polygons, and semantic segmentation. • Applied QA protocols to maintain 98%–98.5% annotation accuracy across delivery pipelines. • Conducted data cleansing to correct edge cases, imbalances, and labeling biases. • Delivered finalized datasets in client-specified formats (JSON, XML, CSV) with review-ready outputs.

2022 - Present

Education

Work History