Segun O.

Key Skills

Software

Other

CVAT

Top Subject Matter

LLM evaluation for engineering/STEM content

Printed Circuit Board (PCB) defect detection

Engineering technical documentation (RAG + RLHF)

Top Data Types

Text

Image

Document

Top Task Types

Object Detection

RLHF

Freelancer Overview

AI Data Trainer & Labeler (Contract) | Remote. Brings 7+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Other, CVAT, and Hugging Face Transformers. Education includes Bachelor of Science, Obafemi Awolowo University (2014). AI-training focus includes data types such as Text, Image, and Document and labeling workflows including Evaluation, Rating, and Object Detection.

Labeling Experience

AI Data Trainer & Labeler (Contract) | Remote

OtherText

Performed rubric-based evaluation, annotation, and ranking of 1,500+ LLM responses to measure factual accuracy, logic, and safety. Audited STEM-focused outputs by checking mathematical accuracy and debugging code-related reasoning to reduce errors. Maintained a 98%+ quality score through strict compliance with granular, rapidly evolving platform guidelines. • LLM response ranking using complex rubrics • Manual review of factuality, logic, and safety • Verification of engineering proofs and code reasoning • Quality audits against evolving annotation standards

2022 - Present

NLP Chatbot for Engineering Technical Documentation | AI Data Specialist

DocumentRLHF

Prepared engineering technical documentation datasets by executing strict preprocessing, tokenization, and manual data labeling. Fine-tuned retrieval-augmented generation outputs by applying RLHF methodologies to evaluate, rank, and rewrite model responses. Focused on eliminating logical hallucinations for production-quality assistance on complex technical manuals. • Manual labeling for RAG fine-tuning • Tokenization and preprocessing for technical documents • RLHF evaluation and response ranking • Output rewriting to prevent hallucinations

2022 - 2022

Computer Vision for Printed Circuit Boards (PCBs) | AI & Hardware Engineer

CVATImageObject Detection

Built and labeled a high-resolution Computer Vision dataset of printed circuit boards for defect detection. Defined custom annotation schemas using precise bounding boxes and semantic segmentation masks for micro-components. Trained and developed CNNs in PyTorch to automate PCB hardware quality assurance using the curated labels. • Bounding box and semantic segmentation labeling for PCB components • Dataset curation for defect detection • Tool-assisted annotation workflows using CVAT/LabelImg • Support for downstream CNN training pipelines

2016 - 2022

Education

Work History