Onyeka Ifeanyi

Key Skills

Software

Other

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Top Label Types

Classification

Object Detection

Text Summarization

Evaluation Rating

Freelancer Overview

I am an analytical Chemical Engineering graduate with hands-on experience in AI model evaluation, prompt engineering, and data annotation for NLP applications. My work involves designing and testing high-variance prompts to assess instruction adherence, factual reliability, and reasoning depth in large language models. I specialize in producing gold-standard reference outputs for supervised fine-tuning (SFT), detecting logical inconsistencies, and identifying hallucinations to improve model accuracy. My technical background includes building API-integrated automation workflows and developing structured content pipelines, as well as strong skills in error pattern detection and systematic response analysis. I thrive in remote, asynchronous environments and bring a detail-oriented, quantitative approach to every AI training data project I undertake.

Labeling Experience

Image object Selection

OtherImageClassificationObject Detection

The project involved image based object identification and selection tasks as part of a computer vision training dataset. The scope included object recognition, spatial awareness validation and precision based selection within cluttered layouts. Each image annotation was completed following defined quality control standards, ensuring high accuracy, consistency and minimal false selections. The project contributed to improving object detection model performance by refining datasets used for supervised computer vision training. Accuracy instruction compliance and annotation consistency were prioritized throughout the workflow.

The project involved image based object identification and selection tasks as part of a computer vision training dataset. The scope included object recognition, spatial awareness validation and precision based selection within cluttered layouts. Each image annotation was completed following defined quality control standards, ensuring high accuracy, consistency and minimal false selections. The project contributed to improving object detection model performance by refining datasets used for supervised computer vision training. Accuracy instruction compliance and annotation consistency were prioritized throughout the workflow.

2025

Fact verification dataset

LabelboxTextClassificationEvaluation Rating

Annotated and evaluated AI generated text outputs across diverse domains including technical and general knowledge prompts. Performed comparative ranking between multiple responses and provided structured justifications for selection decisions. Identified hallucinations, unsupported claims and reasoning gaps to improve dataset quality for supervised fine tuning(SFT).

Annotated and evaluated AI generated text outputs across diverse domains including technical and general knowledge prompts. Performed comparative ranking between multiple responses and provided structured justifications for selection decisions. Identified hallucinations, unsupported claims and reasoning gaps to improve dataset quality for supervised fine tuning(SFT).

2025 - 2025

Education

Work History