Robert Nganga

Key Skills

Software

Anno-Mage

Appen

Argilla

Clickworker

CloudFactory

CVAT

Data Annotation Tech

Google Cloud Vertex AI

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Video

Top Task Types

Audio Recording

Bounding Box

Classification

Computer Programming Coding

Emotion Recognition

Freelancer Overview

Experienced AI Data Specialist with a strong background in data labeling, AI training data preparation, and NLP (Natural Language Processing). Over the past 9 years, I have contributed to the development of high-quality datasets for machine learning models, specializing in text, image, and audio annotation. My expertise includes large language model (LLM) evaluation, text generation, and human-in-the-loop AI systems, ensuring data accuracy and model performance. I have successfully led teams in annotating datasets for speech recognition, computer vision, and NLP tasks, achieving over 95% accuracy in labeled data. My contributions to NLP include evaluating and fine-tuning LLMs for tasks such as text summarization, sentiment analysis, and entity recognition, as well as generating training data for chatbot development. I am proficient in using tools like Labelbox, Prodigy, and Hugging Face, and have a proven track record of delivering high-quality results in fast-paced, collaborative environments. My ability to bridge the gap between raw data and intelligent models has consistently driven improvements in AI system performance and scalability.

Labeling Experience

LLM Evaluation and Text Annotation for Chatbot Development

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Led a team to annotate and evaluate text datasets for the development of a conversational AI chatbot. The project involved: Data Labeling: Annotating text data for entity recognition (NER), sentiment analysis, and intent classification. LLM Evaluation: Evaluating the performance of large language models (e.g., GPT-3, T5) on tasks such as text summarization, question answering, and dialogue generation. Text Generation: Curating and generating high-quality training data to improve chatbot responses. Quality Assurance: Implementing rigorous quality control measures, including inter-annotator agreement checks, to ensure 95%+ accuracy in labeled data. Fine-tuning: Collaborating with AI engineers to fine-tune LLMs based on annotated data, resulting in a 20% improvement in chatbot response quality. The project involved over 50,000 text samples and was completed within a 6-month timeframe.

Led a team to annotate and evaluate text datasets for the development of a conversational AI chatbot. The project involved: Data Labeling: Annotating text data for entity recognition (NER), sentiment analysis, and intent classification. LLM Evaluation: Evaluating the performance of large language models (e.g., GPT-3, T5) on tasks such as text summarization, question answering, and dialogue generation. Text Generation: Curating and generating high-quality training data to improve chatbot responses. Quality Assurance: Implementing rigorous quality control measures, including inter-annotator agreement checks, to ensure 95%+ accuracy in labeled data. Fine-tuning: Collaborating with AI engineers to fine-tune LLMs based on annotated data, resulting in a 20% improvement in chatbot response quality. The project involved over 50,000 text samples and was completed within a 6-month timeframe.

2022 - 2024

Education

Work History