Victor Kariuki

Key Skills

Software

CVAT

Data Annotation Tech

Labelbox

Prodigy

Remotasks

Scale AI

Appen

Top Subject Matter

E-commerce and Retail: AI-driven recommendation systems, dynamic pricing, and customer behavior analysis

Financial Services: Fraud detection, risk assessment, and personalized financial advising using AI

Transportation and Logistics: Intelligent car rental systems, predictive maintenance, and route optimization

Top Data Types

Computer Code Programming

Image

Video

Top Label Types

Computer Programming Coding

Data Collection

Entity Ner Classification

Freelancer Overview

AI Specialist with 5+ years of software engineering experience, specializing in data labeling and AI model training. Expertise in e-commerce, financial services, and transportation sectors. Skilled in designing efficient data labeling workflows and implementing AI solutions, particularly in natural language processing and computer vision. Unique combination of full-stack development and AI knowledge enables seamless integration of AI features into production environments. Experienced in training models for recommendation systems, fraud detection, and intelligent pricing, with a focus on delivering scalable, impactful AI solutions.

Labeling Experience

E-commerce Product Image Annotation for AI-Powered Visual Search

LighttagVideoBounding BoxSegmentation

Led a team in annotating a diverse dataset of e-commerce product images to train an AI-powered visual search engine. The project involved labeling over 100,000 product images across various categories including clothing, electronics, and home goods. Annotations included bounding boxes to identify specific products, classification of product types and attributes, and pixel-level segmentation for precise object boundaries.

Led a team in annotating a diverse dataset of e-commerce product images to train an AI-powered visual search engine. The project involved labeling over 100,000 product images across various categories including clothing, electronics, and home goods. Annotations included bounding boxes to identify specific products, classification of product types and attributes, and pixel-level segmentation for precise object boundaries.

2023 - 2024

Multilingual Audio Annotation for Speech Clarity Optimization

AppenAudioClassificationText Generation

Participated in a multilingual audio annotation project focused on analyzing and refining spoken English data from international speakers. My role involved listening to short voice recordings, transcribing spoken content into well-structured, grammatically accurate text, and categorizing speech features for clarity and fluency. The task required filtering out filler words, identifying patterns in pronunciation, and rephrasing incomplete speech without altering intended meaning. The project included over 12,000 audio samples across various accents. I consistently met accuracy standards above 98% and collaborated closely with QA reviewers to align on consistency checks. Work was performed with structured guidelines and evolving annotation protocols, which required both adaptability and attention to nuance in non-native pronunciation patterns.

Participated in a multilingual audio annotation project focused on analyzing and refining spoken English data from international speakers. My role involved listening to short voice recordings, transcribing spoken content into well-structured, grammatically accurate text, and categorizing speech features for clarity and fluency. The task required filtering out filler words, identifying patterns in pronunciation, and rephrasing incomplete speech without altering intended meaning. The project included over 12,000 audio samples across various accents. I consistently met accuracy standards above 98% and collaborated closely with QA reviewers to align on consistency checks. Work was performed with structured guidelines and evolving annotation protocols, which required both adaptability and attention to nuance in non-native pronunciation patterns.

2023 - 2023

Sentiment Analysis and Intent Classification for Customer Support

Scale AITextClassificationEmotion Recognition

Contributed to a text-based annotation project aimed at enhancing sentiment analysis and intent recognition for a customer support chatbot. Tasks involved classifying sentiment as Positive, Negative, Neutral, or Mixed, labeling intents across categories like Billing Inquiry, Technical Issue, Feedback, and Order Status, and summarizing lengthy customer messages into concise, actionable insights to improve chatbot training.

Contributed to a text-based annotation project aimed at enhancing sentiment analysis and intent recognition for a customer support chatbot. Tasks involved classifying sentiment as Positive, Negative, Neutral, or Mixed, labeling intents across categories like Billing Inquiry, Technical Issue, Feedback, and Order Status, and summarizing lengthy customer messages into concise, actionable insights to improve chatbot training.

2023 - 2023

Education

Work History