Jurnee Jones

Key Skills

Software

AWS SageMaker

CVAT

Google Cloud Vertex AI

Labelbox

Label Studio

Roboflow

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Image

Text

Top Task Types

Computer Programming Coding

Evaluation Rating

Fine Tuning

Prompt Response Writing SFT

Text Generation

Freelancer Overview

With over 5 years of professional experience in software development and cloud engineering, I’ve contributed to projects involving data labeling, AI model evaluation, and large-scale dataset management. My expertise in Python, AWS SageMaker, and Google Cloud Vertex AI has allowed me to build and refine systems that support data collection, labeling automation, and quality assurance for machine learning workflows. I specialize in text-based AI training, LLM evaluation, and code-based data annotation for NLP and code generation models. My strong foundation in Test-Driven Development (TDD), OOP, and cloud deployment enables me to create efficient and accurate labeling solutions that improve AI performance and reliability. I’m passionate about advancing AI systems through precise data labeling, structured evaluation, and ethical model improvement.

Labeling Experience

LLM Text Evaluation and Prompt Optimization Project

Label StudioTextQuestion AnsweringText Generation

Participated in evaluating and refining outputs from large language models (LLMs) used for text generation and summarization tasks. Annotated datasets with accuracy and tone ratings, corrected factual inconsistencies, and created high-quality prompt–response pairs for supervised fine-tuning. Used AWS SageMaker and Label Studio for data management, ensuring consistent formatting, labeling accuracy, and adherence to quality assurance guidelines. The project involved thousands of text samples and contributed to improved model coherence and factual reliability.

Participated in evaluating and refining outputs from large language models (LLMs) used for text generation and summarization tasks. Annotated datasets with accuracy and tone ratings, corrected factual inconsistencies, and created high-quality prompt–response pairs for supervised fine-tuning. Used AWS SageMaker and Label Studio for data management, ensuring consistent formatting, labeling accuracy, and adherence to quality assurance guidelines. The project involved thousands of text samples and contributed to improved model coherence and factual reliability.

2022 - 2023

Education

Work History