Ajimati Abdullahi

Key Skills

Software

Labelbox

CVAT

Surge AI

Appen

Top Subject Matter

Nlp Domain Expertise

E-commerce Product Data & Taxonomy

Marketing Domain Expertise

Top Data Types

Text

Image

Document

Top Task Types

Classification

Bounding Box

RLHF

Freelancer Overview

AI Data Annotation & Labeling Specialist. Brings 8+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Labelbox, CVAT, and Surge AI. Education includes Bachelor of Applied Science, The London School of Economics and Political Science (2021). AI-training focus includes data types such as Text and Image and labeling workflows including Classification, Bounding Box, and RLHF.

Labeling Experience

AI Data Annotation & Labeling Specialist (RLHF)

Surge AITextRLHF

Evaluated AI-generated text responses for correctness, tone, safety, and helpfulness as part of RLHF and model alignment workflows. Compared AI and human preferences to support high-quality reinforcement learning datasets. Used consensus, inter-annotator agreement, and multi-pass review to ensure accurate evaluation. • Reviewed output for harmful content and tone deviations • Ranked and rated model responses for machine learning refinement • Flagged edge cases and provided resolution feedback • Maintained high consistency for RLHF evaluations

Evaluated AI-generated text responses for correctness, tone, safety, and helpfulness as part of RLHF and model alignment workflows. Compared AI and human preferences to support high-quality reinforcement learning datasets. Used consensus, inter-annotator agreement, and multi-pass review to ensure accurate evaluation. • Reviewed output for harmful content and tone deviations • Ranked and rated model responses for machine learning refinement • Flagged edge cases and provided resolution feedback • Maintained high consistency for RLHF evaluations

2022 - Present

AI Data Annotation & Labeling Specialist

CVATImageBounding Box

Annotated product images with bounding boxes and segmentation masks for use in computer vision model pipelines. Carefully labeled visual data to support supervised learning for product recognition and attribute tagging. Conducted visual QA reviews and flagged ambiguous items for project leads. • Labeled thousands of consumer product images across fashion, beauty, and digital products • Applied bounding box and segmentation logic for vision model training • Identified and resolved inconsistencies in visual annotation • Collaborated with teams to refine annotation guidelines

Annotated product images with bounding boxes and segmentation masks for use in computer vision model pipelines. Carefully labeled visual data to support supervised learning for product recognition and attribute tagging. Conducted visual QA reviews and flagged ambiguous items for project leads. • Labeled thousands of consumer product images across fashion, beauty, and digital products • Applied bounding box and segmentation logic for vision model training • Identified and resolved inconsistencies in visual annotation • Collaborated with teams to refine annotation guidelines

2022 - Present

AI Data Annotation & Labeling Specialist

LabelboxTextClassification

Performed high-accuracy text classification, sentiment labeling, and intent tagging as part of training datasets for NLP models. Applied annotation guidelines to ensure data quality and managed edge cases using consensus protocols. Conducted multi-pass QA on batches of 500–2,000+ labeled items and maintained >98% annotation accuracy across all output. • Applied entity classification and intent tagging for AI text training datasets • Evaluated sentiment and tone for text annotation quality • Used structured labeling logic and detailed taxonomies • Delivered high-volume throughput with quality benchmarks

Performed high-accuracy text classification, sentiment labeling, and intent tagging as part of training datasets for NLP models. Applied annotation guidelines to ensure data quality and managed edge cases using consensus protocols. Conducted multi-pass QA on batches of 500–2,000+ labeled items and maintained >98% annotation accuracy across all output. • Applied entity classification and intent tagging for AI text training datasets • Evaluated sentiment and tone for text annotation quality • Used structured labeling logic and detailed taxonomies • Delivered high-volume throughput with quality benchmarks

2022 - Present

Content Operations & Data Quality Specialist

AppenTextClassification

Reviewed, structured, and categorized large volumes of product data, marketing copy, and user-generated content for AI and ML training use. Applied taxonomy frameworks and developed internal annotation guidelines to standardize outputs for downstream pipelines. Identified low-quality or policy-violating content and flagged it during annotation. • Classifying text for categorization and policy compliance • Created QA checklists to guide team labeling • Segmented content by domain and target taxonomy • Managed structured data entry for ML integration

Reviewed, structured, and categorized large volumes of product data, marketing copy, and user-generated content for AI and ML training use. Applied taxonomy frameworks and developed internal annotation guidelines to standardize outputs for downstream pipelines. Identified low-quality or policy-violating content and flagged it during annotation. • Classifying text for categorization and policy compliance • Created QA checklists to guide team labeling • Segmented content by domain and target taxonomy • Managed structured data entry for ML integration

2020 - Present

Education

Work History