Oghenemaro O.

Key Skills

Software

Don't disclose

Top Subject Matter

LLM output evaluation and quality assessment

Video content evaluation for AI training/assessment rubrics

Legal Services & Contract Review

Top Data Types

Text

Video

Image

Document

Top Task Types

Bounding Box

Polygon

Segmentation

Classification

Entity (NER) Classification

Point/Key Point

Polyline

Cuboid

Object Detection

Text Generation

Question Answering

Transcription

Red Teaming

Fine-tuning

RLHF

Text Summarization

Computer Programming/Coding

Evaluation/Rating

Data Collection

Function Calling

Prompt + Response Writing (SFT)

Freelancer Overview

AI Data Evaluator and Annotation Contractor (AI output quality review). Brings 6+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Remotasks (Scale AI), Appen, and Don't disclose. Education includes Bachelor of Engineering, Federal University of Petroleum Resources (FUPRE) (2026) and Software Engineering Programme, DevTeam NG (2020). AI-training focus includes data types such as Text and Video and labeling workflows including Evaluation and Rating.

Labeling Experience

AI Data Evaluator and Annotation Contractor (AI output quality review)

Text

Evaluated 150+ AI-generated outputs for RLHF comparisons, content quality, and text evaluation tasks to assess response quality. Scored and checked LLM responses for correctness, relevance, coherence, and instruction-following, and flagged issues for escalation. Wrote structured rationales documenting the reasoning behind each rating decision. • Contract work via Remotasks (Scale AI) / Appen (remote) • Maintained a 96% average accuracy rating across high-volume evaluation batches • Used rubric-style assessment with compliance checks for every decision • Provided consistent quality control across multiple task formats

2024 - 2025

AI Output Quality Review (video rubric-based evaluation)

Don't discloseVideo

Reviewed egocentric video content at specified timestamps against task rubrics using editorial judgment. Applied zero-assumption assessments to evaluate output quality and consistency with the given task criteria. Escalated ambiguous outputs per rubric guidance. • Rubric-based scoring across video segments • Flagged ambiguous cases for escalation • Ensured accuracy consistency across batches

Not specified

Education

Work History