Key Skills

Software

CVAT

Labelbox

SuperAnnotate

Doccano

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Text

Video

Top Task Types

No task types listed

Freelancer Overview

I have several years of hands-on experience in data labeling and AI training projects, working with video, audio, and text datasets. My expertise includes object tracking in self-driving car imagery, audio transcription with speaker diarization, and NLP tasks such as sentiment analysis and Named Entity Recognition. I have contributed to large-scale projects across industries including autonomous vehicles, healthcare, and conversational AI systems. I consistently deliver high-quality annotations with over 98% accuracy in QA-reviewed tasks, using tools like Labelbox, CVAT, Audacity, and Doccano. My ability to adapt to complex guidelines, work independently or with QA teams, and handle sensitive data sets me apart in this field. I take pride in providing reliable training data that directly supports the performance of machine learning models.

Labeling Experience

Conversational AI – Chat Intent & Entity Labeling

DoccanoTextEntity Ner Classification

Labeled over 10,000 customer service chat messages to train a conversational AI system. Tasks included identifying user intent, extracting named entities (e.g. product names, locations), and tagging sentiment (positive, neutral, negative). Applied strict annotation guidelines to ensure semantic accuracy and domain relevance. Collaborated with AI engineers to refine labeling schemas based on model performance feedback. This dataset helped improve chatbot understanding and response accuracy across multilingual environments. Maintained a consistent labeling accuracy of 97% or higher.

Labeled over 10,000 customer service chat messages to train a conversational AI system. Tasks included identifying user intent, extracting named entities (e.g. product names, locations), and tagging sentiment (positive, neutral, negative). Applied strict annotation guidelines to ensure semantic accuracy and domain relevance. Collaborated with AI engineers to refine labeling schemas based on model performance feedback. This dataset helped improve chatbot understanding and response accuracy across multilingual environments. Maintained a consistent labeling accuracy of 97% or higher.

2024 - 2024

Autonomous Driving Video Annotation – Object Tracking Project

CVATVideoBounding BoxObject Detection

Annotated hundreds of video sequences for an autonomous driving dataset. Tasks involved precise bounding box annotation and multi-frame object tracking for vehicles, pedestrians, and traffic signs in diverse weather and lighting conditions. Followed detailed class guidelines and collaborated with QA reviewers to ensure labeling consistency. The resulting dataset was used to train object detection models for real-time navigation systems. Delivered over 5,000 accurately labeled frames with >98% QA score. Demonstrated strong attention to detail, understanding of motion consistency, and ability to meet tight deadlines.

Annotated hundreds of video sequences for an autonomous driving dataset. Tasks involved precise bounding box annotation and multi-frame object tracking for vehicles, pedestrians, and traffic signs in diverse weather and lighting conditions. Followed detailed class guidelines and collaborated with QA reviewers to ensure labeling consistency. The resulting dataset was used to train object detection models for real-time navigation systems. Delivered over 5,000 accurately labeled frames with >98% QA score. Demonstrated strong attention to detail, understanding of motion consistency, and ability to meet tight deadlines.

2022 - 2023

Education

Work History