Ndizigamye E.

Key Skills

Software

Micro1

Scale AI

Toloka

Clickworker

Top Subject Matter

AI model evaluation

RLHF quality assurance

Data annotation and labeling quality assurance

Top Data Types

Text

Image

Document

Top Task Types

RLHF

Classification

Prompt + Response Writing (SFT)

Freelancer Overview

AI Model Evaluation & RLHF — Self Directed Project. Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include N and A. Education includes Associate of Applied Science, RP Tumba College (2025). AI-training focus includes data types such as Text and Image and labeling workflows including RLHF, Classification, and Prompt + Response Writing (SFT).

Labeling Experience

AI Service Support and Provider — RP TUMBA College

TextPrompt + Response Writing (SFT)

Developed AI training prompts and user-facing documentation to make complex procedures clear and actionable. Built manuals that served as the foundation for AI training prompts by ensuring instructions were clear, concise, and easy to follow. Improved prompt usability so learners could obtain more accurate responses aligned to engineering activities and designs. • AI prompt writing and refinement • Technical documentation for training prompts • Prompt instruction clarity and usability improvements • Support for accurate, engineering-focused AI outputs

2025 - Present

Data Labeling — Precision Data Project (Image Annotation)

ImageClassification

Performed data annotation for image datasets as part of large-scale labeling efforts. Followed detail-oriented verification to maintain consistent label quality for model training. Used quality assurance checks to reduce errors and ensure reliable dataset integrity. • Image dataset labeling • Verification and labeling consistency checks • Error reduction through QA • High-integrity training data support

2022 - 2025

Data Labeling & Quality Assurance — Precision Data Project

TextClassification

Completed large-scale data annotation for text datasets with a strict focus on detail-oriented verification and labeling. Maintained a 98% accuracy rate across high-volume tasks, supporting high-integrity machine learning training. Applied quality assurance practices to ensure labeled outputs were consistent and reliable for downstream evaluation. • Text data labeling and verification • High-volume annotation with accuracy targets • QA-oriented consistency checks • Support for ML training data quality

2022 - 2025

AI Model Evaluation & RLHF — Self Directed Project

TextRLHF

Performed Reinforcement Learning from Human Feedback (RLHF) by ranking and grading AI responses for factual accuracy, safety, and logical consistency. Identified and corrected model hallucinations to ensure outputs matched high-quality human reasoning. Supported model improvement through continuous evaluation and feedback loops for better reliability and neutrality. • RLHF-style response ranking and grading • Hallucination identification and correction • Safety and factual consistency checks • Quality-focused iterative feedback for model improvement

2022 - 2025

Education

Work History