Rafael Rodrigues

Key Skills

Software

AWS SageMaker

Top Subject Matter

Healthcare

Finance

Transport

Top Data Types

Medical Dicom

Top Label Types

Text Summarization

Computer Programming Coding

Data Collection

Freelancer Overview

I bring over a decade of experience in mobile engineering with a strong focus on healthcare, real-time data, and analytics-driven product development, making me well-versed in the requirements of high-quality data labeling and AI training data workflows. My background includes architecting and deploying cross-platform applications that integrate wearable device data (such as heart rate, glucose, and sleep metrics), leveraging GraphQL and REST APIs, and implementing robust data pipelines for real-time alerts and analytics. I have hands-on experience with data collection, annotation, and validation processes, particularly in medical and user engagement domains, and have collaborated closely with backend teams to ensure data integrity and consistency. My expertise in using tools like Firebase Analytics, AWS (Lambda, DynamoDB, S3), and automated testing frameworks (Jest, XCTest) has enabled me to deliver reliable, well-structured datasets for both machine learning and business intelligence purposes. I am passionate about building scalable, accessible solutions and thrive in environments where data quality and precision are critical to product success.

Labeling Experience

AI Data Annotation for Conversational AI Training

TextClassification

Worked on large scale data annotation for training conversational AI systems. The project involved labeling thousands of dialogue samples used to improve intent recognition, entity extraction, and response quality in chatbot models. Responsibilities included classifying user intents, tagging entities such as locations, dates, and products, and annotating conversation flows to help train NLP models. Maintained strict labeling guidelines and performed regular quality checks to ensure high consistency and accuracy across datasets. Collaborated with reviewers to resolve ambiguous cases and improve annotation standards. The annotated datasets were used to train and evaluate machine learning models for customer support and virtual assistant applications. Project size exceeded 50,000 labeled text samples and required careful attention to linguistic context and semantic accuracy.

Worked on large scale data annotation for training conversational AI systems. The project involved labeling thousands of dialogue samples used to improve intent recognition, entity extraction, and response quality in chatbot models. Responsibilities included classifying user intents, tagging entities such as locations, dates, and products, and annotating conversation flows to help train NLP models. Maintained strict labeling guidelines and performed regular quality checks to ensure high consistency and accuracy across datasets. Collaborated with reviewers to resolve ambiguous cases and improve annotation standards. The annotated datasets were used to train and evaluate machine learning models for customer support and virtual assistant applications. Project size exceeded 50,000 labeled text samples and required careful attention to linguistic context and semantic accuracy.

2024 - 2025

Healthcare Data Labeling for Remote Patient Monitoring AI

Aws SagemakerMedical DicomText SummarizationComputer Programming Coding

Worked on a healthcare AI initiative focused on labeling and validating patient-generated health data from wearable devices, including heart rate, glucose levels, sleep metrics, and symptom reports. The project involved annotating time-series data for anomalies, trend changes, and risk indicators, as well as labeling clinical notes and communication logs to support AI-generated summaries for care teams. I collaborated closely with clinicians and backend engineers to define labeling guidelines, ensure consistency, and validate AI outputs against raw data. Emphasis was placed on accuracy, privacy, and reliability, with multiple QA passes and schema validation to ensure labeled data was production-ready and safe for real-world use.

Worked on a healthcare AI initiative focused on labeling and validating patient-generated health data from wearable devices, including heart rate, glucose levels, sleep metrics, and symptom reports. The project involved annotating time-series data for anomalies, trend changes, and risk indicators, as well as labeling clinical notes and communication logs to support AI-generated summaries for care teams. I collaborated closely with clinicians and backend engineers to define labeling guidelines, ensure consistency, and validate AI outputs against raw data. Emphasis was placed on accuracy, privacy, and reliability, with multiple QA passes and schema validation to ensure labeled data was production-ready and safe for real-world use.

2023 - 2025

Education

Work History