Udemeobong A.

Key Skills

Software

Clickworker

Google Cloud Vertex AI

Mindrift

OpenCV AI Kit (OAK)

SuperAnnotate

Toloka

Internal/Proprietary Tooling

iMerit

Top Subject Matter

Programming and code in Javascript, Python

LLM Evaluation in English,

AI Response evaluation

Top Data Types

Computer Code Programming

Image

Video

Text

Document

Top Task Types

Classification

Computer Programming/Coding

Geocoding

Question Answering

Translation/Localization

RLHF

Freelancer Overview

AI Data Reviewer & QA Specialist. Brings 7+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include iMerit, N, and A. Education includes Bachelor of Engineering, Federal University of Technology, Akure (2019). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and RLHF.

Labeling Experience

AI Operations Generalist

Text

Completed higher-complexity AI evaluation tasks after advancing tiers within 3 weeks. Worked on premium multimodal evaluation activities with higher pay rates and strict quality gates. Consistently achieved strong task approval performance across multiple batches. • Participated in 1,500+ cross-modal microtasks covering visual reasoning, dialogue generation, and multimodal evaluation. • Maintained an approval rate above 96% across all batches. • Authored 200+ short-form natural dialogue scripts from video prompts. • Produced training data spanning 12+ real-world interaction scenarios for frontier LLMs.

2026 - Present

AI Data Reviewer & QA Specialist

iMeritText

Reviewed 200–350 AI annotation tasks per day across enterprise-scale AI training pipelines, maintaining a defect catch rate above 94%. Focused on multi-class and multi-modal labelling workflows to improve label quality and reduce downstream errors. Coordinated daily resolution of critical issues affecting annotation reliability. • Performed QA review of annotated outputs for multi-class and multi-modal tasks. • Detected and flagged change-detection failures and ambiguous classifications. • Verified object localisation correctness and corrected over/under-annotation. • Measured impact by cutting downstream rework by ~30% per sprint.

2026 - Present

Data Annotator

Internal/Proprietary ToolingTextPrompt + Response Writing (SFT)RLHF

crafting precise and clear prompts to guide AI systems in generating accurate, contextually appropriate responses

2024 - 2024

Data Annotator

Internal/Proprietary ToolingVideoAction Recognition

Worked on a data annotation project for a tech company focused on action recognition in videos. The role involved manually annotating and labeling video datasets with precise action boundaries, identifying specific actions within each frame, and ensuring accurate temporal tagging. Collaborated with the technical team to define action categories and annotation guidelines, ensuring consistency and clarity across diverse video content. Utilized video annotation tools to mark actions in each frame, supporting the development of machine learning models for real-time action recognition. This work contributed to enhancing the company’s video analysis capabilities, ensuring high-quality training data for action recognition models

2024 - 2024

Data Annotator

Internal/Proprietary ToolingImageBounding Box

Developed and implemented a bounding image classification project for a leading supermarket brand to enable store item detection. The role involved annotating and labeling store items in images using bounding boxes, ensuring accurate categorization across diverse product types. Collaborated with cross-functional teams to establish annotation guidelines and maintain data consistency. Utilized data preprocessing and augmentation techniques to enhance the training dataset, improving model robustness. Conducted quality assurance checks to validate annotations, contributing to a high-performing machine learning model for automated inventory management and product detection

2023 - 2024

Education

Work History