Shradha Sutar

Key Skills

Software

CVAT

SuperAnnotate

Labelbox

Top Subject Matter

Retail Surveillance/Computer Vision/Vision-Language Models

Computer Vision/Retail

Vision-Language Modeling/Human Activity Recognition

Top Data Types

Video

Image

Text

Document

Top Task Types

Bounding Box

Text Generation

Classification

Freelancer Overview

AI Data Evaluator (Computer Vision & VLM). Core strengths include CVAT, SuperAnnotate, and Labelbox. Education includes Post Graduate Diploma, MIT ADT University (2022) and Bachelor of Computer Applications, Savitribai Phule Pune University (2021). AI-training focus includes data types such as Video and Image and labeling workflows including Evaluation, Rating, and Bounding Box.

Labeling Experience

AI Data Evaluator (Computer Vision & VLM)

CVATVideo

As an AI Data Evaluator, I evaluated and reviewed retail surveillance video datasets supporting self-checkout loss-prevention AI systems. My responsibilities included validation of AI-generated captions and assessment of dataset consistency to improve vision-language model training. I identified edge cases and performed dataset labeling, quality auditing, and validation tasks as per project guidelines. • Daily video dataset evaluation and annotation for retail applications • Validation of captions for vision-language models • Identification and documentation of data edge cases and inconsistencies • Quality assurance through systematic dataset review and auditing

As an AI Data Evaluator, I evaluated and reviewed retail surveillance video datasets supporting self-checkout loss-prevention AI systems. My responsibilities included validation of AI-generated captions and assessment of dataset consistency to improve vision-language model training. I identified edge cases and performed dataset labeling, quality auditing, and validation tasks as per project guidelines. • Daily video dataset evaluation and annotation for retail applications • Validation of captions for vision-language models • Identification and documentation of data edge cases and inconsistencies • Quality assurance through systematic dataset review and auditing

2025 - Present

Data Labelling Specialist

SuperannotateImageBounding Box

As a Data Labelling Specialist at Stellarview, I validated and annotated over 50,000 images for classification and bounding box tasks supporting computer vision models. I maintained high accuracy in QA processes and ensured data consistency. My work was instrumental in training and improving computer vision models for commercial use. • Image annotation with bounding box and classification labels • Quality control and dataset validation for high accuracy • Collaboration with team for annotation process improvement • Support for AI model development via dataset QA

As a Data Labelling Specialist at Stellarview, I validated and annotated over 50,000 images for classification and bounding box tasks supporting computer vision models. I maintained high accuracy in QA processes and ensured data consistency. My work was instrumental in training and improving computer vision models for commercial use. • Image annotation with bounding box and classification labels • Quality control and dataset validation for high accuracy • Collaboration with team for annotation process improvement • Support for AI model development via dataset QA

2024 - 2025

Data Annotation Specialist

LabelboxVideoText Generation

As a Data Annotation Specialist at Qualitas Global, I created structured natural-language descriptions of video content for vision-language model training. My tasks included describing human appearance, actions, and interactions according to complex annotation guidelines. I reviewed and corrected AI-generated captions to ensure dataset consistency and quality. • Natural language description creation for video data • Annotation of appearance, actions, and interactions • Caption review and quality validation • Support for training datasets in vision-language applications

As a Data Annotation Specialist at Qualitas Global, I created structured natural-language descriptions of video content for vision-language model training. My tasks included describing human appearance, actions, and interactions according to complex annotation guidelines. I reviewed and corrected AI-generated captions to ensure dataset consistency and quality. • Natural language description creation for video data • Annotation of appearance, actions, and interactions • Caption review and quality validation • Support for training datasets in vision-language applications

2023 - 2024

ML Data Preparation Intern

ImageClassification

As an intern at MIT University, I prepared and organized image datasets for a plant leaf disease classification CNN project. My tasks included dataset preprocessing, dataset validation, and quality assurance to ensure readiness for machine learning model training. I evaluated and refined datasets based on interim model testing results to optimize data quality. • Dataset preparation and validation for image classification tasks • Preprocessing images for CNN model input • Quality assurance checks for training data • Iterative dataset refinement based on model feedback

As an intern at MIT University, I prepared and organized image datasets for a plant leaf disease classification CNN project. My tasks included dataset preprocessing, dataset validation, and quality assurance to ensure readiness for machine learning model training. I evaluated and refined datasets based on interim model testing results to optimize data quality. • Dataset preparation and validation for image classification tasks • Preprocessing images for CNN model input • Quality assurance checks for training data • Iterative dataset refinement based on model feedback

2022 - 2022

Education

Work History