Ann A.

Key Skills

Software

Don't disclose

Labelbox

Label Studio

Top Subject Matter

Prompt bias detection

AI prompt quality evaluation

and training-data quality assurance

Top Data Types

Text

Image

Video

Top Task Types

Data Collection

Polygon

Text Summarization

Transcription

Question Answering

Freelancer Overview

Prompt Engineering Research Intern, Excelerate (2024). Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose, Internal, and Proprietary Tooling. Education includes Bachelor of Science in Computer Science, University Of Ghana. Where I studied Introduction to AI, MAchine Learning and also currently taking an AI course, AI Fluency: Frameworks and Foundation by Anthropic. AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Data Collection.

Labeling Experience

Project Coordinator — AI Chapter, Headstarter AI (2024–Present)

TextData Collection

You coordinated an AI chapter program focused on training-data concepts and responsible AI. You led introductory webinars covering how training data and labeled concepts shape model behavior. You coordinated student projects with structured workflows and quality review processes similar to professional data labeling pipelines. • Guided teams through end-to-end AI project execution with quality checks • Reinforced structured workflows used in labeling and dataset QA • Mentored peers on supervised learning, dataset construction, and RLHF concepts • Educated participants on training-data concepts relevant to annotation work

2024 - Present

Prompt Engineering Research Intern, Excelerate (2024)

Don't discloseText

You worked on prompt engineering research that directly supports AI training data evaluation. You tested and iteratively refined prompts to improve relevance and output accuracy. You identified and documented prompt bias patterns and applied mitigation strategies to improve model fairness. • Designed prompt variations for quality improvement • Evaluated model responses for consistency, coherence, and accuracy • Established prompt evaluation criteria and quality benchmarks • Produced structured documentation of bias findings across test cases

2024

Education

Work History