Karthick Palanichamy

Key Skills

Software

Appen

Clickworker

CVAT

Data Annotation Tech

Labelbox

Label Studio

OneForma

SuperAnnotate

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Video

Top Label Types

Bounding Box

Cuboid

Evaluation Rating

Point Key Point

Polygon

Freelancer Overview

I am a skilled and dependable data annotation specialist with hands-on experience contributing to AI and machine learning training projects. I have worked extensively on text annotation tasks such as entity recognition, intent classification, sentiment analysis, and LLM response evaluation, as well as image annotation for computer vision projects including bounding boxes, segmentation, and OCR-related labeling. I take pride in delivering accurate, well-structured data while strictly following project guidelines. I am comfortable working with different annotation tools and workflows and have experience performing quality checks to maintain high accuracy standards. I adapt quickly to new task types, manage deadlines effectively, and consistently deliver reliable results. I approach every project with a strong sense of ownership and professionalism, and I am committed to supporting AI teams with high-quality training data for both short-term and long-term engagements.

Labeling Experience

QA Specialist

Label StudioVideoPolygonCuboid

Worked as a Video Annotation QA Specialist on healthcare-focused AI projects, ensuring the accuracy and consistency of annotated video datasets used for clinical and medical AI model training. Reviewed video annotations involving object tracking, frame-by-frame labeling, event tagging, and temporal segmentation to support applications such as patient monitoring, medical procedure analysis, and healthcare workflow optimization. Performed detailed quality audits by validating annotations against project guidelines, correcting errors, and providing clear feedback to annotators to improve overall data quality. Ensured compliance with strict accuracy standards and handled sensitive healthcare data responsibly. Consistently maintained high quality scores while supporting scalable and reliable training data for healthcare AI systems.

Worked as a Video Annotation QA Specialist on healthcare-focused AI projects, ensuring the accuracy and consistency of annotated video datasets used for clinical and medical AI model training. Reviewed video annotations involving object tracking, frame-by-frame labeling, event tagging, and temporal segmentation to support applications such as patient monitoring, medical procedure analysis, and healthcare workflow optimization. Performed detailed quality audits by validating annotations against project guidelines, correcting errors, and providing clear feedback to annotators to improve overall data quality. Ensured compliance with strict accuracy standards and handled sensitive healthcare data responsibly. Consistently maintained high quality scores while supporting scalable and reliable training data for healthcare AI systems.

2022 - 2025

Data Labeler

CVATImageBounding BoxPolygon

Contributed to a Bosch autonomous driving project by performing high-accuracy image annotation for road scenes, including vehicles, pedestrians, traffic signs, lane markings, and traffic lights, supporting ADAS and computer vision model training.

Contributed to a Bosch autonomous driving project by performing high-accuracy image annotation for road scenes, including vehicles, pedestrians, traffic signs, lane markings, and traffic lights, supporting ADAS and computer vision model training.

2020 - 2022

Education

Work History