Phillip Brooks

Key Skills

Software

Dataloop

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Document

Image

Top Label Types

Action Recognition

Audio Recording

Classification

Computer Programming Coding

Data Collection

Evaluation Rating

Fine Tuning

Prompt Response Writing SFT

Question Answering

Text Generation

Text Summarization

Transcription

Translation Localization

Freelancer Overview

I am a senior software engineer with a PhD in Computer Science, specializing in AI, data modeling, and large language models (LLMs). My experience spans building and optimizing end-to-end data pipelines, integrating LLMs into real-time fraud detection and document classification systems, and operationalizing AI models for financial and compliance use cases. I have hands-on expertise with Python, TensorFlow, PyTorch, Spark, and cloud platforms like AWS and Azure, ensuring high-quality, secure, and scalable data annotation and labeling workflows. My background includes leading projects that reduced manual review through automation, mentoring teams on best practices for data preparation and model training, and collaborating closely with data scientists to deliver production-ready AI solutions in NLP and predictive modeling. I am passionate about transforming raw data into valuable training datasets that drive accurate, reliable AI systems.

Labeling Experience

Senior Software Engineer, American Express

Internal Proprietary ToolingDocumentClassification

Worked with large transaction datasets to support fraud detection improvements in AI-driven systems. Evaluated and refined annotated training data, enhancing anomaly detection capabilities. Collaborated with AI and data science teams to enforce consistent labeling processes for high-stakes financial data. • Labeled and validated transaction records for fraud detection model training. • Conducted quality assurance reviews on labeled datasets for model accuracy. • Assisted with data preprocessing and structuring for annotation workflows. • Developed internal standards for scalable data labeling in financial systems.

Worked with large transaction datasets to support fraud detection improvements in AI-driven systems. Evaluated and refined annotated training data, enhancing anomaly detection capabilities. Collaborated with AI and data science teams to enforce consistent labeling processes for high-stakes financial data. • Labeled and validated transaction records for fraud detection model training. • Conducted quality assurance reviews on labeled datasets for model accuracy. • Assisted with data preprocessing and structuring for annotation workflows. • Developed internal standards for scalable data labeling in financial systems.

2022 - 2025

Software Engineer, Wells Fargo

Internal Proprietary ToolingTextClassification

Supported AI-based document classification systems by preparing and validating labeled financial datasets. Assisted data scientists with training and evaluation of machine learning models, ensuring that the labeled data met required standards. Helped to improve the quality and consistency of classification outputs by reviewing and updating annotation protocols. • Processed and organized financial text datasets for classification tasks. • Reviewed labeled dataset samples to ensure standardization and accuracy. • Provided feedback on annotation guidelines and workflows. • Automated parts of the document classification review process to reduce manual labor.

Supported AI-based document classification systems by preparing and validating labeled financial datasets. Assisted data scientists with training and evaluation of machine learning models, ensuring that the labeled data met required standards. Helped to improve the quality and consistency of classification outputs by reviewing and updating annotation protocols. • Processed and organized financial text datasets for classification tasks. • Reviewed labeled dataset samples to ensure standardization and accuracy. • Provided feedback on annotation guidelines and workflows. • Automated parts of the document classification review process to reduce manual labor.

2019 - 2022

Software Engineering Tutor, Arizona State University

DataloopTextClassification

Worked as a Software Engineering Tutor focused on guiding students through data labeling projects and model evaluation. Designed and oversaw multiple text classification projects with custom annotation tasks using labeled datasets. Provided direct feedback to students on annotation quality and guideline adherence. • Led tutorials on accurate text data labeling and AI workflow best practices. • Reviewed and scored student-labeled datasets for quality assurance. • Instructed students on fundamentals of text classification and entity annotation. • Assisted students in understanding the impact of quality labels in NLP tasks.

Worked as a Software Engineering Tutor focused on guiding students through data labeling projects and model evaluation. Designed and oversaw multiple text classification projects with custom annotation tasks using labeled datasets. Provided direct feedback to students on annotation quality and guideline adherence. • Led tutorials on accurate text data labeling and AI workflow best practices. • Reviewed and scored student-labeled datasets for quality assurance. • Instructed students on fundamentals of text classification and entity annotation. • Assisted students in understanding the impact of quality labels in NLP tasks.

2016 - 2019

Education

Work History