Ces Ezekiel

Key Skills

Software

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Bounding Box

Segmentation

Classification

Object Detection

Tracking

Freelancer Overview

I am a detail-oriented data annotation specialist with extensive experience supporting AI and machine learning projects across computer vision, NLP, audio, and multimodal domains. My expertise includes image classification, semantic segmentation, entity recognition, text and audio transcription, and LLM evaluation, with a proven track record of maintaining 98%+ accuracy on large-scale datasets. I am highly proficient in industry-standard tools such as Labelbox, CVAT, Label Studio, and Amazon SageMaker Ground Truth, and have contributed to the development of annotation guidelines and quality assurance protocols that improved data consistency and model performance. I excel at following complex instructions, conducting independent research, and collaborating with cross-functional teams to optimize workflows and data quality. My background in computer science and hands-on experience with Python, SQL, and data processing further enable me to deliver high-quality, reliable training data for cutting-edge AI systems.

Labeling Experience

Multi-modal data labeling for AI systems

LabelboxImageBounding BoxSegmentation

Annotated 10,000+ diverse image data points monthly for machine learning model training projects. Performed bounding box annotation, semantic segmentation and image classification tasks across multiple domains. Maintained 98.5% accuracy rate through rigorous quality control protocols and systematic verification processes. Collaborated with data scientists and ML engineers to refine annotation frameworks and improve data quality resulting in a 30% increase in model efficiency. Created comprehensive annotation guidelines ensuring consistency across team members.

Annotated 10,000+ diverse image data points monthly for machine learning model training projects. Performed bounding box annotation, semantic segmentation and image classification tasks across multiple domains. Maintained 98.5% accuracy rate through rigorous quality control protocols and systematic verification processes. Collaborated with data scientists and ML engineers to refine annotation frameworks and improve data quality resulting in a 30% increase in model efficiency. Created comprehensive annotation guidelines ensuring consistency across team members.

2024

Education

Work History