Axel Barraza

Key Skills

Software

Appen

CVAT

Data Annotation Tech

SuperAnnotate

Toloka

Internal/Proprietary Tooling

Top Subject Matter

Imagenes de autos

Evaluacion de textos

Clasificacion de imagenes

Top Data Types

Document

Image

Text

Top Task Types

Bounding Box

Classification

Data Collection

Evaluation Rating

Text Generation

Freelancer Overview

Versatile technical professional with multidisciplinary expertise in electromechanical systems, culinary arts, and front-end web development. Currently specializing in AI data annotation and training, with hands-on experience using industry-standard tools like SuperAnnotate and CVT for text classification and data labeling projects. My technical skill set includes Python scripting for data preprocessing automation, along with strong proficiency in HTML, CSS, and JavaScript for developing optimized web interfaces for machine learning recognition. I have successfully contributed to AI training projects including GUM GUM (focused on text translation and annotation) and NEXUS (involving labeled web page development for AI system training). My background demonstrates rapid learning adaptability, transitioning seamlessly between technical domains while maintaining high-quality standards and professional responsibility. I thrive in dynamic environments that challenge my problem-solving abilities and allow me to apply my diverse skill set to innovative technological solutions. Seeking opportunities where I can leverage my unique combination of technical expertise, data annotation experience, and creative problem-solving skills to contribute to cutting-edge projects in AI development and web technologies.

Reviews (1)

Labeling Experience

Gum Gum

SuperannotateTextClassification

The GUM GUM project involved text annotation and labeling for automated translation systems. As a data annotator, I specialized in classifying, structuring, and tagging multilingual text data to improve the accuracy of language models. Using SuperAnnotate, I meticulously labeled semantic elements, syntax patterns, and contextual relationships within text corpora. This precise annotation work enabled the training of more efficient translation algorithms and enhanced the AI's ability to handle nuanced linguistic structures, contributing to higher-quality automated translation outputs across different language pairs.

The GUM GUM project involved text annotation and labeling for automated translation systems. As a data annotator, I specialized in classifying, structuring, and tagging multilingual text data to improve the accuracy of language models. Using SuperAnnotate, I meticulously labeled semantic elements, syntax patterns, and contextual relationships within text corpora. This precise annotation work enabled the training of more efficient translation algorithms and enhanced the AI's ability to handle nuanced linguistic structures, contributing to higher-quality automated translation outputs across different language pairs.

2025 - 2025

Nexus

SuperannotateDocumentQuestion AnsweringText Generation

The NEXUS project focused on developing specially labeled web interfaces for artificial intelligence training. As a front-end developer and data annotation specialist, I designed and built web pages using HTML, CSS, and JavaScript, optimizing their structure and semantics to make them easily interpretable by machine learning models. I implemented Python scripts to automate data preprocessing workflows and utilized SuperAnnotate for precise component labeling and annotation. This approach enabled effective AI training in recognizing web patterns and improved the system's ability to understand and process complex digital layouts and interactive elements.

The NEXUS project focused on developing specially labeled web interfaces for artificial intelligence training. As a front-end developer and data annotation specialist, I designed and built web pages using HTML, CSS, and JavaScript, optimizing their structure and semantics to make them easily interpretable by machine learning models. I implemented Python scripts to automate data preprocessing workflows and utilized SuperAnnotate for precise component labeling and annotation. This approach enabled effective AI training in recognizing web patterns and improved the system's ability to understand and process complex digital layouts and interactive elements.

2025 - 2025

Education

Work History