James Gutib

Key Skills

Software

AWS SageMaker

CVAT

Remotasks

V7 Labs

Other

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Geospatial Tiled Imagery

Image

Top Label Types

Bounding Box

Cuboid

Polygon

Segmentation

Freelancer Overview

I have over seven years of hands-on experience in data labeling and AI training data across a wide range of annotation types and platforms. My background includes LiDAR annotation and 3D segmentation, image and video annotation, bounding box labeling (2D and video), polygon and image segmentation, skeletal keypoint annotation, and geolocation annotation. I have worked extensively with tools and platforms such as Remotasks, Kognic, V7 Labs, and CVAT, supporting computer vision models used in autonomous driving, mapping, and advanced AI applications. What sets me apart is my versatility and consistency in delivering high-quality annotations across complex and large-scale projects. I adapt quickly to new tools, guidelines, and quality standards, and I have experience handling detailed, accuracy-critical tasks such as LiDAR point cloud labeling and multi-frame video annotations. My long-term experience has strengthened my attention to detail, productivity, and ability to collaborate within remote, QA-driven environments.

Labeling Experience

Data Annotator

V7 LabsImageBounding BoxPolygon

The project involved large-scale AI training data preparation focused on computer vision tasks across multiple platforms. The scope included LiDAR annotation and 3D segmentation, 2D and video bounding box annotation, image and polygon segmentation, skeletal keypoint labeling, and geolocation annotation. Data types ranged from images and videos to LiDAR point clouds, supporting use cases such as autonomous driving, object detection, and spatial analysis. Projects were executed using tools such as Remotasks, Kognic, V7 Labs, and CVAT, with varying levels of complexity and volume. Strict quality measures were followed, including detailed annotation guidelines, frame-by-frame consistency checks, and multi-stage QA reviews. Accuracy, completeness, and adherence to client-specific standards were prioritized, with regular feedback loops and rework processes to ensure high-quality outputs. Productivity targets and quality benchmarks were consistently met while handling complex annotations and

The project involved large-scale AI training data preparation focused on computer vision tasks across multiple platforms. The scope included LiDAR annotation and 3D segmentation, 2D and video bounding box annotation, image and polygon segmentation, skeletal keypoint labeling, and geolocation annotation. Data types ranged from images and videos to LiDAR point clouds, supporting use cases such as autonomous driving, object detection, and spatial analysis. Projects were executed using tools such as Remotasks, Kognic, V7 Labs, and CVAT, with varying levels of complexity and volume. Strict quality measures were followed, including detailed annotation guidelines, frame-by-frame consistency checks, and multi-stage QA reviews. Accuracy, completeness, and adherence to client-specific standards were prioritized, with regular feedback loops and rework processes to ensure high-quality outputs. Productivity targets and quality benchmarks were consistently met while handling complex annotations and

2018 - 2025

Education

Work History