Marcilinus O.

Key Skills

Software

Appen

CrowdFlower

CloudFactory

CrowdSource

Data Annotation Tech

LabelImg

Internal/Proprietary Tooling

Top Subject Matter

AI model training (LLM) and prompt/response evaluation

Survey and data collection for AI training

Legal Services & Contract Review

Top Data Types

Text

Document

Image

Top Task Types

Data Collection

Classification

Question Answering

Text Generation

Fine-tuning

Freelancer Overview

AI Data Labeling & Training Contributor (Outlier AI). Brings 8+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Outlier AI and CrowdGen. Education includes Master of Science, Enugu State University of Science and Technology (2023) and Bachelor of Science, Enugu State University of Science and Technology (2019). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Data Collection.

Labeling Experience

AI Survey & Data Collection Contributor (CrowdGen)

TextData Collection

Supported AI survey and data collection efforts by contributing to CrowdGen task workflows with a focus on collecting and validating textual information. Followed instructions to ensure collected data met quality standards required for AI training and research. Helped maintain compliance and confidentiality when handling potentially sensitive project data. • Participated in CrowdGen survey and data collection projects • Collected textual data according to task instructions • Validated data consistency to support dataset integrity • Maintained confidentiality and compliance for sensitive data

2024 - Present

AI Data Labeling & Training Contributor (Outlier AI)

Text

Provided AI model training support by evaluating AI-generated responses for accuracy, relevance, grammar, safety, and contextual understanding in accordance with project guidelines. Performed prompt assessment and ranking to improve Large Language Model (LLM) performance through iterative feedback. Annotated and categorized textual data, ensuring consistent labeling and dataset integrity for downstream model reliability. • Evaluated response quality across accuracy, relevance, grammar, safety, and context • Conducted prompt assessment and ranking tasks for LLM improvement • Annotated and categorized text using project-specific rules • Reviewed and corrected labeling inconsistencies to maintain dataset quality

2024 - Present

Education

Work History