Iddy Odero

Key Skills

Software

Appen

CloudFactory

Data Annotation Tech

Lionbridge

Surge AI

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Image

Text

Top Task Types

Action Recognition

Emotion Recognition

Evaluation Rating

Freelancer Overview

I have hands-on experience in data labeling and AI training support, having worked with Teach2Give where I contributed to building high-quality datasets for natural language processing (NLP) tasks. My responsibilities included annotating large volumes of text data, categorizing intent, sentiment, and context, as well as verifying model outputs for accuracy and consistency. This helped improve the performance of machine learning models used in conversational AI applications. I collaborated remotely with AI trainers and developers, using tools like Google Colab, Git, and shared annotation platforms, ensuring alignment with evolving project guidelines. What sets me apart is my strong foundation in Python and NLP, coupled with certifications in Prompt Engineering, Data Science, and Natural Language Processing from Coursera, edX, and DataCamp. I bring not only technical precision but also a deep understanding of how annotated data shapes model behavior. I'm currently pursuing a BSc in IT at Kirinyaga University, and I’m passionate about ethical, human-centered AI development. My blend of technical training, real-world annotation experience, and a proactive learning mindset makes me a valuable asset for any AI training data team.

Labeling Experience

Multilingual NLP Prompt Annotation for Conversational AI

Data Annotation TechTextEntity Ner ClassificationClassification

Participated in an NLP-focused project aimed at training a multilingual conversational AI assistant. Responsibilities included annotating thousands of text samples for intent classification, sentiment/emotion tagging, and named entity recognition (NER). Additionally, contributed to prompt writing, quality scoring of AI-generated responses, and reinforcement learning with human feedback (RLHF). The data was used to fine-tune and align large language models. Worked remotely with a distributed team, ensuring high accuracy, consistency, and adherence to annotation guidelines. Quality assurance involved peer reviews and periodic audits.

Participated in an NLP-focused project aimed at training a multilingual conversational AI assistant. Responsibilities included annotating thousands of text samples for intent classification, sentiment/emotion tagging, and named entity recognition (NER). Additionally, contributed to prompt writing, quality scoring of AI-generated responses, and reinforcement learning with human feedback (RLHF). The data was used to fine-tune and align large language models. Worked remotely with a distributed team, ensuring high accuracy, consistency, and adherence to annotation guidelines. Quality assurance involved peer reviews and periodic audits.

2023

Education

Work History