Byishimo K.

Key Skills

Software

No software listed

Top Subject Matter

Multimodal AI training and evaluation (image/text/audio/video)

LLM/AI response evaluation and iterative training support (multimodal tasks)

Multimodal AI training and evaluation (audio tasks)

Top Data Types

Image

Top Task Types

Entity (NER) Classification

Freelancer Overview

AI Trainer (Freelance, Remote) – AI performance improvement and data labeling/evaluation (2026). Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Multimango. Education includes Bachelor of Science in Computer Science and Information Technology, Kampala University (2025). AI-training focus includes data types such as Image and labeling workflows including Evaluation, Rating, and Named Entity Recognition.

Labeling Experience

AI Product Evaluator (Freelance) - Freelance

ImageEntity (NER) Classification

Built and improved AI-enabled products through prompt creation, analysis, and iterative quality improvements while supporting multimodal workflows. Applied analytical skills to identify failure patterns and guide changes to enhance responses across image, text, audio, and video inputs. Collaborated in a remote setup to ensure outputs met expected quality standards using established evaluation practices. • Designed prompts for multimodal inputs • Performed error analysis and iteration to improve output quality • Supported labeling and evaluation-related workflows to refine performance • Reviewed results and adjusted approaches based on evaluation findings

2026 - Present

AI Trainer (Freelance, Remote) – AI performance improvement and data labeling/evaluation (2026)

Image

You trained and evaluated AI responses by reviewing outputs and applying response evaluation to measure performance. You performed prompt-driven testing to understand how the model behaves under different instructions. Findings from error analysis were used to refine prompts and improve the quality of generated responses. • Evaluated response quality against expected behavior. • Tested prompts and assessed changes in output performance. • Analyzed failure patterns in incorrect or low-quality answers. • Updated labeling/prompting inputs to raise overall quality.

2026 - 2026

Education

Work History