Evanscarson Jesejeri

Key Skills

Software

AWS SageMaker

Appen

Data Annotation Tech

Labelbox

Prodigy

Scale AI

SuperAnnotate

Supervisely

V7 Labs

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Text

Top Task Types

Bounding Box

Classification

Computer Programming Coding

Entity Ner Classification

Prompt Response Writing SFT

Freelancer Overview

I am a highly experienced Data Labeling and AI Training Specialist with over 5 years of hands-on expertise in natural language processing (NLP), large language model (LLM) evaluation, and image/video annotation. I have worked extensively on diverse projects, from evaluating AI-generated text for coherence and safety to annotating complex visual data for self-driving technology. My work consistently meets high standards of accuracy, attention to detail, and ethical alignment—core values in AI development. My experience includes prompt/response rating for multilingual LLMs (English and Swahili), semantic segmentation and bounding box labeling for computer vision models, and content moderation for social platforms. I’m skilled with industry-standard tools like Labelbox, Scale AI, Amazon SageMaker, and SuperAnnotate. I bring strong linguistic and analytical skills, adaptability, and a proven ability to deliver high-quality training data for cutting-edge AI systems.

Labeling Experience

Multilingual LLM Evaluation and Prompt Rating Project

AppenTextBounding BoxClassification

Participated in a high-volume LLM prompt/response evaluation project involving English and Swahili datasets. Rated AI-generated responses based on helpfulness, accuracy, safety, bias, and ethical alignment. Tasks included ranking completions, rewriting prompts for better clarity, fine-tuning conversational responses, and flagging unsafe outputs. Contributed to reinforcement learning from human feedback (RLHF) pipelines used to align models with human values. Handled over 20,000 text pairs, with a 98% agreement rate in QA review. Ensured linguistic clarity, neutrality, and alignment with OpenAI’s safety and quality standards. Delivered consistent, high-quality feedback under tight deadlines while maintaining language sensitivity across multiple dialects.

Participated in a high-volume LLM prompt/response evaluation project involving English and Swahili datasets. Rated AI-generated responses based on helpfulness, accuracy, safety, bias, and ethical alignment. Tasks included ranking completions, rewriting prompts for better clarity, fine-tuning conversational responses, and flagging unsafe outputs. Contributed to reinforcement learning from human feedback (RLHF) pipelines used to align models with human values. Handled over 20,000 text pairs, with a 98% agreement rate in QA review. Ensured linguistic clarity, neutrality, and alignment with OpenAI’s safety and quality standards. Delivered consistent, high-quality feedback under tight deadlines while maintaining language sensitivity across multiple dialects.

2019 - 2024

Education

Work History