Enock J.

Key Skills

Software

Data Annotation Tech

Labelbox

Scale AI

CrowdSource

AWS SageMaker

Anno-Mage

Top Subject Matter

Healthcare – Medical records, patient data, clinical notes, diagnostic imaging

E-commerce – Product catalogs, customer reviews, inventory data, product images

Finance – Transaction data, customer feedback, financial documents, fraud detection data

Top Data Types

Image

Text

Document

Top Task Types

Bounding Box

Segmentation

Text Generation

Fine-tuning

Red Teaming

Computer Programming/Coding

Data Collection

Classification

Object Detection

Evaluation/Rating

Prompt + Response Writing (SFT)

Freelancer Overview

Chief Technology Officer. Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Education includes High School Diploma, Nakuru High School (2022) and Bachelor of Science in Information Technoplogy, University of Eldoret (2026). I have hands-on experience in data labeling, data annotation, and dataset preparation for machine learning and AI projects, including image, text, audio, and video annotation. My work involves data cleaning, quality assurance, bounding box annotation, semantic segmentation, text classification, entity recognition, sentiment labeling, and metadata tagging to ensure high-quality training datasets. I am skilled in maintaining annotation accuracy, consistency, taxonomy guidelines, data validation, and workflow optimization while meeting project deadlines and quality standards.

Labeling Experience

AI Training Data Annotation for Computer Vision & NLP Models

TextBounding Box

Worked on a large-scale data annotation project focused on preparing high-quality datasets for machine learning and artificial intelligence models. Responsibilities included image annotation, bounding box labeling, polygon annotation, semantic segmentation, text classification, sentiment analysis, named entity recognition (NER), metadata tagging, and dataset validation. Ensured annotation accuracy, quality assurance, consistency checks, taxonomy compliance, and guideline adherence while handling diverse data types including images, text, audio, and video. Collaborated within annotation workflows to improve dataset quality, model training performance, and data pipeline efficiency for computer vision and natural language processing applications.

2026 - Present

E-commerce Product Image Annotation

ImageBounding Box

Annotated product images for object detection models by labeling items such as clothing, electronics, household goods, and accessories using precise bounding boxes. Maintained high annotation accuracy, followed labeling guidelines, performed quality checks, and ensured dataset consistency for training inventory recognition and product recommendation systems.

2025 - 2026

Customer Feedback Sentiment Analysis Dataset

TextSegmentation

Labeled customer reviews, survey responses, and social media comments into sentiment categories such as positive, negative, and neutral for natural language processing models. Conducted text classification, data validation, consistency reviews, and quality assurance to improve AI-driven customer insights and feedback analysis systems.

2025 - 2025

Education

Work History