Elijah M.

Key Skills

Software

CVAT

Labelbox

Top Subject Matter

LLM training pipeline content evaluation and annotation

Generative AI concepts and AI content labeling/evaluation

Technology / AI

Top Data Types

Text

Document

Image

Top Task Types

Text Generation

Freelancer Overview

Data Annotation Contributor | Career flow (NDA Project). Brings 24+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose and Other. Education includes Bachelor of Science, Makerere University (2005) and Cisco Certified Network Associate, Cisco (2005). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Text Generation.

Labeling Experience

AI Training Contributor | Live AI data annotation & Generative AI training (described in summary)

OtherTextText Generation

Contributed to AI data labeling and evaluation activities through live AI data annotation projects after completing hands-on Generative AI training. Assisted in assessing and improving the quality of AI-generated content for downstream use. Followed guidance on accuracy and safety expectations typical of LLM evaluation tasks. • Participated in live AI data annotation projects • Applied Generative AI concepts during annotation work • Helped verify output quality against evaluation expectations • Completed hands-on Generative AI training

Present

CVAT TRAINING

ImageBounding Box

Annotated residential property images (interior and exterior) using CVAT. Applied bounding box labeling on architectural features, household objects, and outdoor surroundings to build training data for object detection models. Focused on label consistency, boundary accuracy, and batch quality control.

2026 - 2026

Data Labeling Job Simulation

TextClassification

I just completed Forage Academy's Data Labeling on Forage. In the simulation I: * Completed a job simulation where I worked as a Data Labeling Analyst for a hypothetical AI company, reviewing real customer support messages * Practiced classifying messages for Intent, Sentiment, and PII (personally identifiable information) using a consistent labeling schema * Evaluated tricky or ambiguous edge cases, and wrote short rationales justifying final label decisions * Reviewed labeling work done by others, identified inconsistencies, and suggested one improvement to the team’s labeling guidelines * Strengthened my ability to apply clear definitions, follow ethical privacy practices, and maintain consistency across data * Gained hands-on experience in human-AI collaboration, foundational to roles in AI ops, trust & safety, and machine learning support

2026 - 2026

Education

Work History