Juan Ramos

Key Skills

Software

Mindrift

Scale AI

Snorkel AI

Toloka

Telus

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Medical Dicom

Text

Top Task Types

Prompt Response Writing SFT

Question Answering

Freelancer Overview

I have over three years of hands-on experience in data labeling and AI training, contributing to the development and refinement of machine learning models across diverse domains including natural language processing, computer vision, and content moderation. My work has included text classification, image annotation, sentiment analysis, named entity recognition, and evaluation of AI-generated responses for accuracy and relevance. I am highly skilled at maintaining labeling consistency, understanding task guidelines in depth, and providing detailed feedback to improve model output quality. In previous projects, I collaborated with interdisciplinary teams to annotate and review large datasets with precision and speed, ensuring alignment with project-specific taxonomies and quality standards. My strengths include a strong eye for detail, excellent written communication, and the ability to adapt to evolving instructions and complex ontologies. My background in [insert your area of expertise here, e.g., biology, medicine, linguistics, etc.] has also allowed me to work on specialized AI projects that required domain knowledge and critical thinking.

Labeling Experience

Medical Text Annotation for Clinical AI Model Training

Scale AIComputer Code ProgrammingText GenerationComputer Programming Coding

Worked on a large-scale clinical NLP project aimed at training AI to extract and understand medical information from unstructured text such as EHRs and clinical notes. Responsibilities included labeling entities like symptoms, diagnoses, medications, and procedures; classifying clinical outcomes; and summarizing patient histories for downstream tasks. Maintained high inter-annotator agreement (>95%) by closely following detailed medical annotation guidelines and participating in regular calibration sessions. The project spanned over 50,000 data points and contributed directly to the training of clinical decision support models used by healthcare institutions.

Worked on a large-scale clinical NLP project aimed at training AI to extract and understand medical information from unstructured text such as EHRs and clinical notes. Responsibilities included labeling entities like symptoms, diagnoses, medications, and procedures; classifying clinical outcomes; and summarizing patient histories for downstream tasks. Maintained high inter-annotator agreement (>95%) by closely following detailed medical annotation guidelines and participating in regular calibration sessions. The project spanned over 50,000 data points and contributed directly to the training of clinical decision support models used by healthcare institutions.

2021 - 2023

Education

Work History