Matías Cardozo

Key Skills

Software

Labelbox

Scale AI

Google Cloud Vertex AI

Top Subject Matter

Product Data Classification

Top Data Types

Text

Document

Top Task Types

Text Generation

Fine-tuning

Classification

Freelancer Overview

AI Trainer. Brings 8+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Bachelor of Science, Philippines Christian University (2017). AI-training focus includes data types such as Text and labeling workflows including Classification.

Labeling Experience

Senior AI Engineer - Data Labeling and Prompt Evaluation (Bold Estimation)

LabelboxTextFine Tuning

As a Senior AI Engineer at Bold Estimation, I designed and managed LLM training and annotation workflows for improving NLP model performance. I refined prompt engineering, established annotation guidelines, and standardized labeling practices across multi-platform tools. I analyzed model outputs for errors, performed data quality audits, mentored trainers on review methods, and optimized model evaluation routines. • Led the creation of training datasets and edge-case annotation standards using Labelbox and Scale AI. • Conducted A/B and benchmarking tests to validate prompt and labeling improvements. • Developed QA and data audit scripts for large-scale training data reviews in Python and SQL. • Enhanced model fairness, safety, and consistency through targeted dataset design and bias monitoring.

As a Senior AI Engineer at Bold Estimation, I designed and managed LLM training and annotation workflows for improving NLP model performance. I refined prompt engineering, established annotation guidelines, and standardized labeling practices across multi-platform tools. I analyzed model outputs for errors, performed data quality audits, mentored trainers on review methods, and optimized model evaluation routines. • Led the creation of training datasets and edge-case annotation standards using Labelbox and Scale AI. • Conducted A/B and benchmarking tests to validate prompt and labeling improvements. • Developed QA and data audit scripts for large-scale training data reviews in Python and SQL. • Enhanced model fairness, safety, and consistency through targeted dataset design and bias monitoring.

2022 - Present

AI Engineer – Data Annotation & Evaluation

TextFine Tuning

At Aptive Environmental, I designed, processed, and reviewed annotated text datasets to train and validate AI models. I evaluated NLP outputs, established and maintained custom labeling benchmarks, and executed QA for label drift and error modes. I contributed to prompt template creation and audit routines to improve classification accuracy and consistency. • Used spreadsheets and Python to maintain high annotation quality and reviewer notes. • Defined labeling acceptance criteria and operationalized reporting for continuous model improvement. • Performed structured error analysis for edge cases, bias, and hallucination. • Built and tested NLP training workflows leveraging TensorFlow and SQL.

At Aptive Environmental, I designed, processed, and reviewed annotated text datasets to train and validate AI models. I evaluated NLP outputs, established and maintained custom labeling benchmarks, and executed QA for label drift and error modes. I contributed to prompt template creation and audit routines to improve classification accuracy and consistency. • Used spreadsheets and Python to maintain high annotation quality and reviewer notes. • Defined labeling acceptance criteria and operationalized reporting for continuous model improvement. • Performed structured error analysis for edge cases, bias, and hallucination. • Built and tested NLP training workflows leveraging TensorFlow and SQL.

2019 - 2022

AI Trainer – Text Data Annotation

TextClassification

Supported the labeling and review of product data within Python-assisted workflows to enhance AI classification systems. Validated generated AI outputs against specific written guidelines and requirements to ensure consistency and accuracy. Used Pandas to identify anomalies in datasets before training cycles commenced. • Labeled over 8,000 product data records to support AI content quality improvement. • Evaluated outputs for assumptions, completeness, and compliance with project standards. • Provided feedback on ambiguous and edge cases to reduce rework and improve annotation consistency. • Collaborated across teams to refine review criteria and improve data labeling standards.

Supported the labeling and review of product data within Python-assisted workflows to enhance AI classification systems. Validated generated AI outputs against specific written guidelines and requirements to ensure consistency and accuracy. Used Pandas to identify anomalies in datasets before training cycles commenced. • Labeled over 8,000 product data records to support AI content quality improvement. • Evaluated outputs for assumptions, completeness, and compliance with project standards. • Provided feedback on ambiguous and edge cases to reduce rework and improve annotation consistency. • Collaborated across teams to refine review criteria and improve data labeling standards.

2018 - 2019

Education

Work History