Ekemini Essien

AI Prompt Engineer and Annotation Specialist with over three years of dedicated experience in enhancing Large Language Model (LLM) performance. Proven ability in designing and refining prompts, executing rigorous fact-checking, and conducting structured evaluations to significantly improve model accuracy and reduce hallucinations. Highly skilled in a variety of annotation platforms and AI tools, with a passion for developing ethical, user-aligned AI systems.

Eager to leverage expertise in NLP workflows and data generation for innovative freelance projects in the Generative AI sector.

AI Prompt Engineer & Data Annotator (Freelance) at GIDEONTECH.CO (2025-01 – Present)

• Developed and optimized over 500 prompts for a range of NLP tasks, including summarization, classification, and data extraction, leading to a 15% improvement in LLM accuracy.
• Designed and implemented a library of reusable prompt templates, which successfully reduced prompt creation time by 35% across various projects.
• Conducted detailed fact-checking and quality analysis of AI-generated content to identify and correct factual errors, biases, and hallucinations.

Data Annotator & AI Content Reviewer at TELUS International (2024-01 – 2025-12)

• Review and tag over 10,000 NLP outputs for quality assurance, classification, and sentiment analysis tasks.
• Identify and flag 1,200+ biased or hallucinated model outputs, enhancing overall dataset quality and ethical compliance.
• Deliver structured feedback that is directly used to refine model scoring and evaluation protocols.
• Strong ability to follow detailed guidelines without deviation
• High consistency across repeated evaluation tasks
• Comfortable working with structured review workflows
• Able to identify when outputs are misleading despite sounding correct
• Willing to escalate unclear cases instead of guessing

AI Data Contributor at Appen / Crowdgen (2022-01 – 2023-12)

• Labeled and verified over 20,000 text, audio, and image samples for complex NLP and computer vision projects.
• Participated in prompt tuning exercises for LLM training, contributing to a 17% reduction in annotation errors.
• Consistently exceeded quality and throughput targets in a fast-paced, project-based environment.

B.Sc. in Accountancy – Akwa Ibom State University, Nigeria (2020-01 – 2024-11)