Kaushal Javangula

Key Skills

Software

CrowdSource

CVAT

Data Annotation Tech

Dataloop

Doccano

Labelbox

Label Studio

OpenCV AI Kit (OAK)

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Document

Image

Text

Video

Top Label Types

Audio Recording

Classification

Computer Programming Coding

Data Collection

Emotion Recognition

Evaluation Rating

Fine Tuning

Function Calling

Object Detection

Prompt Response Writing SFT

Question Answering

RLHF

Text Generation

Text Summarization

Transcription

Translation Localization

Freelancer Overview

I am an AI and business intelligence professional with hands-on experience in building, processing, and validating high-quality datasets for machine learning and NLP applications. My background includes developing scalable NLP pipelines to process over 115,000 unstructured documents, applying advanced text vectorization, and leveraging unsupervised machine learning for automated data categorization and content labeling. I have worked extensively with Python, SQL, NLTK, and modern frameworks like LangChain and Hugging Face Transformers to build robust data annotation workflows and sentiment analysis models. My expertise spans data cleaning, semantic embedding, and quality assurance for training data, with proven success in generating actionable insights and supporting AI-driven solutions across domains such as business analytics, customer feedback, and digital operations.

Labeling Experience

Content Reviewer & Data Quality Specialist

Data Annotation TechImageClassification

Reviewed and processed over 115,000 pieces of content daily, focusing on evaluating quality and relevance for AI model training. Classified and categorized content into over 1,400 categories following detailed guidelines to ensure consistency and accuracy. Utilized systematic comparison methods to identify duplicate or similar data and improve dataset reliability. • Applied strict content labeling protocols to maintain a 98% accuracy rate. • Delivered feedback to enhance team classification processes and guideline clarity. • Worked with large-scale datasets in high-volume, deadline-driven environments. • Supported continual improvement of machine learning content pipelines.

Reviewed and processed over 115,000 pieces of content daily, focusing on evaluating quality and relevance for AI model training. Classified and categorized content into over 1,400 categories following detailed guidelines to ensure consistency and accuracy. Utilized systematic comparison methods to identify duplicate or similar data and improve dataset reliability. • Applied strict content labeling protocols to maintain a 98% accuracy rate. • Delivered feedback to enhance team classification processes and guideline clarity. • Worked with large-scale datasets in high-volume, deadline-driven environments. • Supported continual improvement of machine learning content pipelines.

2025 - 2025

Research Analyst & Content Evaluator

CVATTextClassification

Analyzed and categorized more than 2,000 user feedback responses for sentiment and theme extraction. Evaluated program data across business segments, identifying key trends and labeling actionable insights for machine learning purposes. Synthesized feedback information and classified results for model training accuracy improvement. • Applied entity and sentiment classification tasks on user-generated feedback. • Reviewed complex datasets for accuracy and pattern recognition relevant to AI development. • Delivered classified findings to stakeholders for further model enhancement. • Maintained precision and consistency across all annotation work for project timelines.

Analyzed and categorized more than 2,000 user feedback responses for sentiment and theme extraction. Evaluated program data across business segments, identifying key trends and labeling actionable insights for machine learning purposes. Synthesized feedback information and classified results for model training accuracy improvement. • Applied entity and sentiment classification tasks on user-generated feedback. • Reviewed complex datasets for accuracy and pattern recognition relevant to AI development. • Delivered classified findings to stakeholders for further model enhancement. • Maintained precision and consistency across all annotation work for project timelines.

2024 - 2024

Data Review Specialist

LabelboxTextClassification

Reviewed thousands of structured data entries daily, identifying and correcting errors and inconsistencies to ensure clean data for machine learning models. Labeled and categorized system issues according to strict protocols, reducing issue resolution time. Maintained accuracy across large-scale datasets to support AI system performance. • Applied classification labeling to technical text and system issue domains. • Performed rigorous quality assurance on annotation outputs. • Trained team members on labeling standards and best practices. • Maintained high precision and reliability throughout labelling processes.

Reviewed thousands of structured data entries daily, identifying and correcting errors and inconsistencies to ensure clean data for machine learning models. Labeled and categorized system issues according to strict protocols, reducing issue resolution time. Maintained accuracy across large-scale datasets to support AI system performance. • Applied classification labeling to technical text and system issue domains. • Performed rigorous quality assurance on annotation outputs. • Trained team members on labeling standards and best practices. • Maintained high precision and reliability throughout labelling processes.

2021 - 2023

Education

Work History