Enomat Abraham

Key Skills

Software

Appen

Clickworker

CVAT

Data Annotation Tech

Datatroniq

Labelbox

Label Studio

OneForma

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Audio

Computer Code Programming

Document

Geospatial Tiled Imagery

Image

Text

Video

Top Label Types

Action Recognition

Audio Recording

Bounding Box

Computer Programming Coding

Data Collection

Entity Ner Classification

Fine Tuning

Geocoding

Mapping

Polygon

Prompt Response Writing SFT

Question Answering

Text Generation

Text Summarization

Tracking

Transcription

Translation Localization

Freelancer Overview

I have over 6 years of experience working at the intersection of AI, data annotation, and web development, with a strong focus on creating high-quality datasets for computer vision and NLP projects. My background includes hands-on annotation of images, videos, and text using tools like CVAT, Labelbox, and Supervisely, covering tasks such as object detection, segmentation, NER, and sentiment analysis. I have contributed to AI training data for diverse domains including smart surveillance, agriculture, and chatbot systems, always ensuring data accuracy and consistency. My technical skills in Python, prompt engineering, and generative AI models, combined with my collaborative and detail-oriented approach, enable me to deliver reliable, ethical datasets that power innovative AI solutions.

Labeling Experience

Computer Vision Data Labeling & QA for Multi-Use Image and Video Datasets

CVATImageBounding BoxEntity Ner Classification

Worked on end-to-end data labeling and dataset curation for computer vision models using image and video data. The project involved annotating datasets for object detection, classification, and segmentation tasks using CVAT and Labelbox. Responsibilities included creating and following detailed annotation guidelines, labeling bounding boxes and segmentation masks, reviewing annotations for consistency, and handling edge cases and ambiguous samples. Performed quality assurance through spot checks, consistency reviews, and iterative guideline refinement to reduce label noise and ensure high-quality training data. The datasets supported downstream model training, evaluation, and performance improvement.

Worked on end-to-end data labeling and dataset curation for computer vision models using image and video data. The project involved annotating datasets for object detection, classification, and segmentation tasks using CVAT and Labelbox. Responsibilities included creating and following detailed annotation guidelines, labeling bounding boxes and segmentation masks, reviewing annotations for consistency, and handling edge cases and ambiguous samples. Performed quality assurance through spot checks, consistency reviews, and iterative guideline refinement to reduce label noise and ensure high-quality training data. The datasets supported downstream model training, evaluation, and performance improvement.

2023 - 2025

Education

Work History