Ifeanyi Moses

Key Skills

Software

Appen

Axiom AI

Clickworker

CVAT

Dataloop

Doccano

HiveMind

iMerit

Label Studio

Redbrick AI

Remotasks

Roboflow

Scale AI

Trilldata Technologies

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Document

Text

Top Task Types

Computer Programming Coding

Data Collection

Prompt Response Writing SFT

Text Summarization

Translation Localization

Freelancer Overview

Versatile AI Trainer and Security Engineer with hands-on experience in AI data labeling, model evaluation, and dataset curation across cybersecurity, cloud infrastructure, and technical NLP domains. Skilled in annotating structured and unstructured data such as security logs, phishing samples, cloud configurations, and network events to support the development of AI-driven security and automation systems. Bringing a deep technical foundation from cybersecurity and cloud engineering, I combine analytical precision with real-world domain knowledge to enhance AI training accuracy. I’ve contributed to projects involving LLM output evaluation, intrusion detection datasets, and technical text classification, ensuring that model training data is both relevant and secure. Passionate about improving AI understanding of complex technical environments while maintaining high standards of data quality, integrity, and privacy.

Labeling Experience

Cybersecurity and Cloud Infrastructure Data Labeling Project

DoccanoTextEntity Ner ClassificationRelationship

Contributed to the annotation and quality assurance of AI datasets used for cybersecurity automation and cloud threat detection. Tasks involved labeling structured and unstructured data such as phishing samples, network logs, cloud misconfiguration alerts, and vulnerability descriptions. Annotated entities and relationships between attack techniques, assets, and indicators of compromise (IOCs) to support NLP-based threat analysis and LLM fine-tuning. Ensured data consistency, integrity, and relevance through detailed review and cross-validation with established frameworks (MITRE ATT&CK, OWASP). Maintained high annotation accuracy (>98%) by following strict labeling guidelines and feedback loops. Collaborated on evaluation tasks for LLM-generated responses related to security events and risk categorization.

Contributed to the annotation and quality assurance of AI datasets used for cybersecurity automation and cloud threat detection. Tasks involved labeling structured and unstructured data such as phishing samples, network logs, cloud misconfiguration alerts, and vulnerability descriptions. Annotated entities and relationships between attack techniques, assets, and indicators of compromise (IOCs) to support NLP-based threat analysis and LLM fine-tuning. Ensured data consistency, integrity, and relevance through detailed review and cross-validation with established frameworks (MITRE ATT&CK, OWASP). Maintained high annotation accuracy (>98%) by following strict labeling guidelines and feedback loops. Collaborated on evaluation tasks for LLM-generated responses related to security events and risk categorization.

2024

LLM Evaluation and Technical Text Classification Project

AppenTextClassificationQuestion Answering

Participated in the evaluation and annotation of AI-generated text responses for large language model (LLM) fine-tuning. Tasks included classifying and rating model outputs for correctness, clarity, factual accuracy, and tone. Labeled large volumes of text related to technology, cloud computing, and general knowledge topics to improve model reliability and response quality. Performed detailed text categorization and summarization, ensuring each annotation adhered to strict quality control standards. Provided human feedback (RLHF) to train models in prompt understanding, question answering, and content generation for educational and enterprise use cases. Achieved consistent accuracy benchmarks and contributed to dataset refinement for multiple LLM iterations.

Participated in the evaluation and annotation of AI-generated text responses for large language model (LLM) fine-tuning. Tasks included classifying and rating model outputs for correctness, clarity, factual accuracy, and tone. Labeled large volumes of text related to technology, cloud computing, and general knowledge topics to improve model reliability and response quality. Performed detailed text categorization and summarization, ensuring each annotation adhered to strict quality control standards. Provided human feedback (RLHF) to train models in prompt understanding, question answering, and content generation for educational and enterprise use cases. Achieved consistent accuracy benchmarks and contributed to dataset refinement for multiple LLM iterations.

2024 - 2024

Education

Work History