Jaleel Mwero

Key Skills

Software

CrowdSource

Doccano

Encord

Figure Eight

Google Cloud Vertex AI

HiveMind

iMerit

Img Lab

Labelbox

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Audio

Computer Code Programming

Document

Geospatial Tiled Imagery

Medical Dicom

Text

Video

Top Label Types

Action Recognition

Classification

Computer Programming Coding

Evaluation Rating

Fine Tuning

Text Generation

Text Summarization

Transcription

Translation Localization

Freelancer Overview

I am a results-driven professional with hands-on experience at the intersection of AI training data, data structuring, and user experience design. My background spans data labeling, content classification, and annotation workflows aligned with real-world product development in e-commerce, education, and health-focused digital platforms. I specialize in transforming complex requirements into structured, high-quality datasets that support machine learning models, while maintaining consistency, accuracy, and annotation guidelines. I bring strong analytical skills, attention to detail, and a deep understanding of data quality, privacy, and compliance. With experience documenting datasets using metadata standards and applying data protection principles, I ensure AI training data is reliable, secure, and scalable. I thrive in collaborative environments where precision, quality control, and continuous improvement are critical to delivering impactful AI solutions.

Labeling Experience

AI Training Data Annotation & Text Classification

LabelboxTextEntity Ner ClassificationClassification

Annotated and classified large volumes of unstructured text data for AI training, including intent labeling, content categorization, and named entity recognition (NER). Followed detailed annotation guidelines to ensure consistency and high inter-annotator agreement. Performed quality assurance checks, error analysis, and dataset validation to improve labeling accuracy. Contributed structured, high-quality datasets used to train and evaluate language models supporting search, recommendations, and automated customer interactions.

Annotated and classified large volumes of unstructured text data for AI training, including intent labeling, content categorization, and named entity recognition (NER). Followed detailed annotation guidelines to ensure consistency and high inter-annotator agreement. Performed quality assurance checks, error analysis, and dataset validation to improve labeling accuracy. Contributed structured, high-quality datasets used to train and evaluate language models supporting search, recommendations, and automated customer interactions.

2023 - 2024

Education

Work History