Solomon Egwu

Key Skills

Software

Scale AI

Surge AI

Appen

Remotasks

Labelbox

Argilla

CVAT

Prodigy

CrowdSource

Top Subject Matter

Llms Domain Expertise

code tasks

Reasoning Domain Expertise

Top Data Types

Text

Document

Computer Code Programming

Top Task Types

RLHF

Prompt + Response Writing (SFT)

Text Generation

Fine-tuning

Computer Programming/Coding

Freelancer Overview

AI Data Annotator & Evaluator — Freelance. Brings 11+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Scale AI, Surge AI, and Appen. Education includes Master of Science, Northumbria University (2022) and Bachelor of Science, University of Sunderland (2015). AI-training focus includes data types such as Text and labeling workflows including RLHF, Prompt + Response Writing (SFT), and Evaluation.

Labeling Experience

AI Data Annotator & Evaluator — Freelance

Scale AITextRLHF

Engaged in annotating instruction-response pairs, preference ranking, prompt writing, and red-teaming tasks dedicated to LLM fine-tuning and evaluation. Conducted comprehensive review and labeling for AI-generated outputs, focusing on helpfulness, harmlessness, honesty, and technical accuracy. Produced high volumes of labeled data supporting safety and model alignment across reasoning, coding, and creative domains. • Annotated instruction-response pairs for LLM fine-tuning • Performed pairwise preference ranking for RLHF • Authored prompts and seed data for model training • Participated in red-teaming and guideline validation

Engaged in annotating instruction-response pairs, preference ranking, prompt writing, and red-teaming tasks dedicated to LLM fine-tuning and evaluation. Conducted comprehensive review and labeling for AI-generated outputs, focusing on helpfulness, harmlessness, honesty, and technical accuracy. Produced high volumes of labeled data supporting safety and model alignment across reasoning, coding, and creative domains. • Annotated instruction-response pairs for LLM fine-tuning • Performed pairwise preference ranking for RLHF • Authored prompts and seed data for model training • Participated in red-teaming and guideline validation

2023 - Present

Prompt Writer & Model Reviewer — Freelance

Scale AITextPrompt Response Writing SFT

Developed prompts and reviewed model completions for early-stage LLM training, emphasizing mathematical and logical reasoning applications. Scored outputs for adherence to reference facts and stepwise rationale, supporting both factual and instruction-following dataset creation. Drafted annotation notes and initial guidelines for safety labeling workflows. • Crafted prompts for logic and math reasoning • Assessed model outputs for factual correctness and consistency • Provided annotation samples for task decomposition • Documented safety issues and contributed to guidelines

Developed prompts and reviewed model completions for early-stage LLM training, emphasizing mathematical and logical reasoning applications. Scored outputs for adherence to reference facts and stepwise rationale, supporting both factual and instruction-following dataset creation. Drafted annotation notes and initial guidelines for safety labeling workflows. • Crafted prompts for logic and math reasoning • Assessed model outputs for factual correctness and consistency • Provided annotation samples for task decomposition • Documented safety issues and contributed to guidelines

2022 - 2022

Content Reviewer & Dataset Contributor — Freelance

Scale AIText

Reviewed text Q&A data and quality-checked NLP training datasets for software engineering, networking, and computer science domains. Tested adherence to instructions and flagged ambiguous or misaligned outputs for annotation resolution. Contributed to the evaluation of topic coverage and identification of gaps in model training data. • Quality-checked Q&A pairs in technical domains • Flagged and annotated ambiguous training responses • Assessed instruction-following in early-stage language models • Helped review datasets for subject matter completeness

Reviewed text Q&A data and quality-checked NLP training datasets for software engineering, networking, and computer science domains. Tested adherence to instructions and flagged ambiguous or misaligned outputs for annotation resolution. Contributed to the evaluation of topic coverage and identification of gaps in model training data. • Quality-checked Q&A pairs in technical domains • Flagged and annotated ambiguous training responses • Assessed instruction-following in early-stage language models • Helped review datasets for subject matter completeness

2021 - 2022

Education

Work History