Emmanuel E.

Key Skills

Software

Label Studio

Scale AI

Remotasks

CVAT

Don't disclose

Top Subject Matter

AI evaluation and cybersecurity-adjacent technical analysis

Cybersecurity labs

Technical documentation for cybersecurity practice

Top Data Types

Text

Image

Audio

Document

Computer Code Programming

Top Task Types

Data Collection

Prompt + Response Writing (SFT)

Text Summarization

Classification

Text Generation

Object Detection

Function Calling

Red Teaming

Freelancer Overview

AI data evaluation and response evaluation (self-directed projects / coursework). Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose and GitHub. Education includes Higher National Diploma, Institute of Management and Technology (IMT) (2023) and N/A, Institute of Cybersecurity Professionals (2024). AI-training focus includes data types such as Computer Code, Programming, and Document and labeling workflows including Evaluation, Rating, and Red Teaming.

Labeling Experience

Windows security and privilege escalation lab practice

Don't discloseDocumentRed Teaming

Performed controlled cybersecurity lab practice focused on Windows security and privilege escalation. This training supports development of evaluation and error-identification skills relevant to assessing AI-generated or technical outputs. The documented lab work is positioned as practical exercises that complement AI evaluation learning. • Practice Windows security concepts in labs • Perform privilege escalation in controlled environments • Strengthen troubleshooting and debugging skills • Use documentation to reinforce technical learning

2026 - Present

AI data evaluation and response evaluation (self-directed projects / coursework)

Don't disclose

Engaged in AI evaluation and response assessment by practicing structured reasoning to review technical outputs and identify errors. This involves evaluating AI responses and applying a disciplined approach to determine correctness and quality. The experience is described as ongoing learning and practical experimentation with AI data evaluation workflows. • Review technical outputs for accuracy and errors • Evaluate AI response quality using structured reasoning • Learn AI evaluation workflows and data-related tasks • Apply Python-based analysis for evaluation and automation

2026 - Present

GitHub-based documentation of AI-adjacent technical practice

DocumentPrompt + Response Writing (SFT)

Created technical documentation for cybersecurity learning labs and practical exercises using GitHub. The documentation process supports structured reasoning and consistent reporting of technical outcomes. This helps build the habits and format needed for later AI evaluation and training-style workflows. • Maintain lab notes in a versioned repository • Summarize practical exercises and outcomes • Improve clarity and correctness of technical explanations • Prepare materials suitable for structured review

2023 - 2024

Education

Work History