Faith Nduta

Key Skills

Software

CVAT

Data Annotation Tech

Lionbridge

Mindrift

Remotasks

Scale AI

Toloka

Top Subject Matter

LLM Training and Evaluation

Software Development and Quality Assurance

Mathematical Content Creation and Enhancement

Top Data Types

Computer Code Programming

Document

Medical Dicom

Top Task Types

Bounding Box

Computer Programming Coding

Evaluation Rating

Prompt Response Writing SFT

Question Answering

Freelancer Overview

I am a seasoned AI Trainer and Data Labeling Specialist with over seven years of experience in training large language models (LLMs) and developing high-quality AI training data. My expertise includes writing and evaluating prompts and responses, summarizing content, and performing rigorous quality assurance. I have successfully utilized popular data labeling tools such as Labelbox, SuperAnnotate, Scale AI, and Amazon SageMaker Ground Truth to enhance the performance and accuracy of AI systems. My strong background in software development, with proficiency in Python, TypeScript, JavaScript, C++, and Java, complements my data labeling skills. I have a proven track record of leading AI model enhancement projects and implementing automated QA processes, significantly improving efficiency and accuracy. My mathematical acumen further supports my ability to generate and refine complex AI training data, ensuring precision and reliability in all my work.

Labeling Experience

Advanced Text Classification for LLM Training

Scale AIComputer Code ProgrammingPrompt Response Writing SFT

I led an extensive data labeling project focused on advanced text classification for training large language models. The project involved creating and refining over 1,000 prompts and responses, categorizing text data, summarizing content, and performing rigorous quality assurance to ensure the highest standards of accuracy and consistency. The use of Labelbox, SuperAnnotate, and Scale AI was integral in managing and executing the labeling tasks efficiently. This project significantly improved the performance and accuracy of the AI models, contributing to a 20% increase in overall model efficiency.

I led an extensive data labeling project focused on advanced text classification for training large language models. The project involved creating and refining over 1,000 prompts and responses, categorizing text data, summarizing content, and performing rigorous quality assurance to ensure the highest standards of accuracy and consistency. The use of Labelbox, SuperAnnotate, and Scale AI was integral in managing and executing the labeling tasks efficiently. This project significantly improved the performance and accuracy of the AI models, contributing to a 20% increase in overall model efficiency.

2023

LLM Data Labeling and Quality Assurance

MindriftComputer Code ProgrammingClassificationFine Tuning

I was involved in a comprehensive project focused on training and evaluating large language models (LLMs). The project encompassed various data labeling tasks, including text classification, text summarization, prompt writing, and response generation. My responsibilities included crafting high-quality prompts and responses, summarizing large volumes of text, and performing detailed quality assurance to ensure the accuracy and relevance of the data. The project was extensive, requiring the processing and evaluation of thousands of data points, and adherence to stringent quality measures to maintain the integrity of the AI models.

I was involved in a comprehensive project focused on training and evaluating large language models (LLMs). The project encompassed various data labeling tasks, including text classification, text summarization, prompt writing, and response generation. My responsibilities included crafting high-quality prompts and responses, summarizing large volumes of text, and performing detailed quality assurance to ensure the accuracy and relevance of the data. The project was extensive, requiring the processing and evaluation of thousands of data points, and adherence to stringent quality measures to maintain the integrity of the AI models.

2024 - 2024

Education

Work History