Ajumobi J.

Key Skills

Software

Label Studio

Top Subject Matter

NLP data labeling (sentiment, intent, NER) and supervised dataset curation.

Computer vision annotation (object detection and segmentation).

LLM response evaluation and RLHF preference feedback.

Top Data Types

Text

Image

Document

Top Task Types

Entity (NER) Classification

Object Detection

RLHF

Freelancer Overview

AI Data Labeller — Atlas (Text/NLP Annotation). Brings 4+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Label Studio, Internal, and Proprietary Tooling. Education includes Bachelor of Science, Federal University of Oye (2009). AI-training focus includes data types such as Text and Image and labeling workflows including Entity (NER) Classification, Object Detection, and RLHF.

Labeling Experience

AI Data Labeller — Atlas (RLHF Feedback/Response Ranking)

TextRLHF

Performed Reinforcement Learning from Human Feedback (RLHF) annotation by rating and ranking AI-generated responses. Evaluated responses on helpfulness, harmlessness, and honesty to generate training data for large language model alignment. • Rated responses to capture quality and safety alignment signals. • Ranked multiple responses to support preference learning. • Applied safety/policy considerations when flagging non-compliant content. • Fed curated feedback into reward model training workflows.

2024 - 2024

AI Data Labeller — Atlas (Object Detection Annotation)

Label StudioImageObject Detection

Created detailed image annotations to build training datasets for supervised computer vision models. Labeled objects using bounding boxes and segmentation masks while adhering to strict pixel-accuracy requirements. • Produced bounding boxes for object localization. • Added segmentation masks for fine-grained object delineation. • Maintained annotation quality to support reliable model learning. • Helped sustain daily throughput targets without sacrificing guideline compliance.

2024 - 2024

AI Data Labeller — Atlas (Text/NLP Annotation)

Label StudioTextEntity (NER) Classification

Produced high-quality text annotations for supervised machine learning datasets under strict labeling guidelines. Labeled sentiment, intent, named entities, and relevant boundaries to support downstream model training and evaluation. • Annotated multi-class sentiment and intent categories for text samples. • Applied NER tagging to identify entities within customer and AI-generated content. • Followed detailed guideline criteria to maintain accuracy above 98%. • Participated in QA dispute resolution to improve labeling consistency.

2024 - 2024

Education

Work History