Izuegbu S.

Key Skills

Software

SuperAnnotate

Other

Top Subject Matter

Responsible AI evaluation and safety/quality assessment of AI outputs

NLP dataset annotation and QA validation for NLP tasks

Computer vision dataset annotation and QA

Top Data Types

Text

Image

Document

Top Task Types

Segmentation

Freelancer Overview

SuperAnnotate | Data Annotator/QA Expert (AI Evaluation Specialist focus). Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include SuperAnnotate and Other. Education includes Bachelor of Science, Federal University of Technology (2021) and Certification, Aptech (2025). AI-training focus includes data types such as Text and Image and labeling workflows including Evaluation, Rating, and Entity (NER).

Labeling Experience

SuperAnnotate | Data Annotator/QA Expert (AI Evaluation Specialist focus)

SuperAnnotateText

Evaluated AI-generated text responses for quality, coherence, factual accuracy, and adherence to safety guidelines using structured rubrics across high-volume task cycles. Identified and escalated harmful, biased, or policy-violating content, maintaining consistent judgment standards even for ambiguous edge cases. Produced detailed written rationale for preference decisions using side-by-side comparisons of AI response pairs to support model improvement. • Quality and safety rubric-based evaluation • Harmful content and policy violation identification/escalation • Side-by-side response preference comparison with written rationale • Iterative feedback incorporation to align with evolving platform guidelines

2025 - 2026

HandShake AI | Data Annotator/Evaluator

OtherImageSegmentation

Labeled image datasets for computer vision models including bounding boxes, segmentation masks, and attribute tagging across vehicles, medical imagery, and retail product categories. Performed multi-pass QA checks to catch and correct labeling errors prior to dataset handoff. Documented annotation progress and raised edge cases to project leads as part of the delivery and quality process. • Bounding box, segmentation mask, and attribute tagging • Multi-pass QA review for image labeling correctness • Category coverage across vehicles/medical/retail images • Progress logging and edge case escalation

2025 - 2025

HandShake AI | Data Annotator/Evaluator

OtherText

Annotated large-scale text datasets for NLP tasks including named entity recognition (NER), coreference resolution, and question-answer pair generation with reported accuracy above 97%. Conducted multi-pass QA reviews to identify and correct labeling errors before final dataset delivery, reducing error rates by an average of 18%. Maintained detailed annotation logs, flagged edge cases to project leads, and prepared end-of-sprint quality summary reports within remote-first asynchronous workflows. • NER, coreference resolution, and QA pair generation labeling • Multi-pass QA verification and error correction • Edge case logging/flagging and sprint quality reporting • Remote async annotation execution and milestone tracking

2025 - 2025

Education

Work History