I help AI companies build better models through precise data annotation, document analysis, and quality-controlled labeling pipelines. Engineering background with hands-on experience in NLP, RLHF evaluation, structured data systems, and production software development.
I am a data annotation and AI training specialist with a B.Eng in Mechanical Engineering and a strong background in software development. I work at the intersection of human intelligence and machine learning, providing the high-quality labeled data that AI models depend on to perform accurately.
My engineering training gives me an edge in structured thinking, pattern recognition, and maintaining precision across large volumes of work. I have designed data systems that process thousands of records with strict validation, built quality control pipelines, and created annotation-ready datasets for over 3,000 items with trait classification, attribute tagging, and visual quality review.
As a full-stack developer, I understand how annotated data flows into ML pipelines. I don't just label data — I understand why the labels matter, how they affect model performance, and what quality standards are needed to produce reliable training sets.
I maintain accuracy standards above 95% across all workflows and have 3+ years of experience working independently in remote, distributed teams across time zones.
Precise bounding box annotation on PDFs and complex documents. Entity classification, table extraction, key-value pair identification, reading order mapping, and header/caption labeling across multi-format layouts.
Text classification, named entity recognition, sentiment analysis, and intent labeling. Fluent in English with native proficiency in Yoruba and Pidgin for multilingual AI training data.
Human feedback for reinforcement learning. Response ranking, preference labeling, conversational AI evaluation, red teaming, and output quality assessment for large language models.
Reviewing and evaluating AI-generated code for correctness, efficiency, and style. Bug identification, complexity reasoning, and providing ground-truth solutions in Python, JavaScript, TypeScript, and Solidity.
Image classification, object detection labeling, attribute tagging, and visual quality review. Experience annotating thousands of items with structured metadata and consistency checks.
Building QC workflows with gold standard questions, inter-annotator agreement tracking, consensus labeling, and automated accuracy monitoring. I deliver clean data, not just labeled data.
B.Eng in Mechanical Engineering combined with full-stack development experience. I approach annotation with the same precision I bring to writing production code — systematic, accurate, and consistent at scale.
I don't just label data. As a developer who builds systems that consume structured data, I understand how annotation quality directly impacts model performance. Every label I apply is informed by that context.
Track record processing 50-60 daily tasks with consistent quality, creating 3,000+ annotated items, and managing databases with thousands of structured records. I maintain accuracy above 95% regardless of volume.
3+ years working independently in remote, async teams across time zones. I follow complex guidelines precisely, flag edge cases proactively, and deliver on time without supervision.
Fluent English with native Yoruba and Nigerian Pidgin. Valuable for multilingual AI training, African language NLP projects, and cross-cultural content evaluation.
Available full-time or part-time with flexible hours and immediate start. Equipped with personal hardware, stable internet, and accounts on Payoneer, PayPal, and Wise for seamless payments.
Engineering degree providing strong foundations in analytical thinking, mathematical modeling, structured problem solving, and technical documentation. These skills translate directly into high-accuracy data annotation, complex document analysis, and systematic quality control.
Looking for a reliable annotation specialist or AI training contributor? I am available for projects of any size — from small pilot batches to ongoing annotation work.