John K.

Key Skills

Software

Don't disclose

Appen

Top Subject Matter

Kiswahili language curricula

linguistic syntax modeling

educational diagnostics

Top Data Types

Text

Image

Audio

Video

Document

Top Task Types

Entity (NER) Classification

Prompt + Response Writing (SFT)

Fine-tuning

Transcription

Tracking

Freelancer Overview

Lead Kiswahili Educator & Educational Data Systems Coordinator (Mangu High School). Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose. Education includes Postgraduate Program, Kenyatta University (2023) and Bachelor of Education (Arts), Zetech University (2023). AI-training focus includes data types such as Text, Image, and Audio and labeling workflows including Entity (NER) Classification, Prompt + Response Writing (SFT), and Fine-tuning.

Labeling Experience

Lead Kiswahili Educator & Educational Data Systems Coordinator (Mangu High School)

Don't discloseTextEntity (NER) Classification

Built structured diagnostic and assessment pipelines that transform unstructured linguistic learning resources into labeled digital training items for student evaluation. Applied natural language parsing concepts to create consistent text models aligned with Swahili grammar and competency-based learning outcomes. Supported ongoing supervised-like analysis by tracking predictive assessment signals derived from labeled curricular content. • Labeling/structuring of learner-related text artifacts • Diagnostic identification of learning discrepancies via labeled pipelines • Predictive tracking for assessment outcomes • Cross-functional guidance using structured linguistic environments

2024 - Present

Data Annotator Specialist & Prompt Engineering Consultant (Independent AI Contracting & Infrastructure Portfolios)

Don't discloseVideoTracking

Annotated video assets as part of high-density multimodal datasets to support supervised training and temporal understanding. Used detailed labeling metadata to represent visual elements consistently across video content. Supported model evaluation by ensuring labeled targets align with high-quality benchmark parameters. • Video data labeling within multimodal dataset pipelines • Temporal/visual labeling support for training • Metadata-driven consistency across video samples • Benchmark-aligned evaluation contribution

2023 - Present

Data Annotator Specialist & Prompt Engineering Consultant (Independent AI Contracting & Infrastructure Portfolios)

Don't discloseAudioTranscription

Performed audio labeling within multimodal datasets to facilitate supervised training for speech-related AI components. Applied structured labeling metadata to support consistent training targets and evaluation of acoustic/linguistic correctness. Contributed labeled audio assets to end-to-end response evaluation using benchmark quality criteria. • Audio data labeling with consistent metadata • Support for speech-oriented supervised training • Evaluation support for response quality metrics • Contribution to multimodal labeling pipelines

2023 - Present

Data Annotator Specialist & Prompt Engineering Consultant (Independent AI Contracting & Infrastructure Portfolios)

Don't discloseImageFine-tuning

Labeled image components within high-density multimodal datasets to support supervised training and downstream fine-tuning for computer vision models. Defined and applied annotation metadata to ensure consistent training targets across visual samples. Integrated labeling outputs into evaluation workflows to validate model response quality under benchmark parameters. • Image data labeling for supervised multimodal training • Consistent meta-tag/annotation schema application • Integration of labeled data into training/evaluation pipelines • Quality-oriented benchmark compliance support

2023 - Present

Data Annotator Specialist & Prompt Engineering Consultant (Independent AI Contracting & Infrastructure Portfolios)

Don't discloseTextPrompt + Response Writing (SFT)

Designed and executed annotation pipelines for high-density datasets to enable supervised training of localized language systems and multimodal AI engines. Produced precise meta-tag definitions and labeled data for text-intensive tasks supporting LLM fine-tuning workflows. Developed prompt engineering libraries with conditional instructions to guide model behavior and improve logical consistency while reducing anomalous outputs. • Text annotation with meta-tag schemas for supervised training • Prompt engineering and instruction design for LLM execution • Evaluation of outputs for syntactic correctness and Swahili dialect alignment • Compliance checking against benchmark quality parameters

2023 - Present

Education

Work History