Pelumi Akintibubo

Key Skills

Software

Appen

Clickworker

CVAT

Data Annotation Tech

Deep Systems

Img Lab

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Video

Top Task Types

Data Collection

Evaluation/Rating

Fine-tuning

Question Answering

Text Summarization

Freelancer Overview

My experience in the field of AI training data and data labeling is both broad and deep, encompassing work on major Large Language Models (LLMs) and specialized computer vision tasks. A significant achievement involved contribution to the training pipeline for a prominent model, similar to LLAMA, where I played a critical role in curating, annotating, and validating extensive English language datasets. This work required a keen understanding of natural language processing (NLP) principles, focusing on generating and refining high-quality prompt-response pairs to enhance the model's conversational fluency, factual accuracy, and overall utility in answering diverse user queries. Furthermore, I contributed to the development and refinement of a WhatsApp AI chatbot, which honed my skills in generating targeted dialogue examples and ensuring the AI's responses were contextually appropriate and user-friendly. What sets my experience apart is this blend of expertise across different AI modalities. On the computer vision front, I am proficient in various annotation techniques crucial for model training. I possess strong skills in image segmentation, labeling, and object identification, utilizing industry-standard tools to accurately delineate boundaries and classify objects within complex visual data. Specifically, I have extensive experience with box annotation for object detection and tracking, including meticulous mapping and capture of detailed information displayed on-screen in

Labeling Experience

AI Data Labeling Expert

Data Annotation TechVideoQuestion AnsweringText Generation

The project involved a comprehensive AI training data creation and validation initiative, encompassing both Natural Language Processing (NLP) and Computer Vision tasks. The primary objective was to build and refine robust datasets for training large-scale, multimodal AI systems. The NLP scope covered generating and validating vast quantities of conversational text data in English to enhance the AI's ability to understand context, generate coherent responses, and prevent undesirable outputs. There was strict adherence to project-specific style guides and language guidelines to prevent grammatical errors and maintain a consistent linguistic standard in the training data.

The project involved a comprehensive AI training data creation and validation initiative, encompassing both Natural Language Processing (NLP) and Computer Vision tasks. The primary objective was to build and refine robust datasets for training large-scale, multimodal AI systems. The NLP scope covered generating and validating vast quantities of conversational text data in English to enhance the AI's ability to understand context, generate coherent responses, and prevent undesirable outputs. There was strict adherence to project-specific style guides and language guidelines to prevent grammatical errors and maintain a consistent linguistic standard in the training data.

2021 - 2025

Education

Work History