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Clinton Nyandigita

Clinton Nyandigita

Data Scientist | LLMs & Agentic AI Specialist | AI Training & Annotation

Kenya flagNairobi, Kenya
$15.00/hrIntermediateRoboflowAws SagemakerHivemind

Key Skills

Software

RoboflowRoboflow
AWS SageMakerAWS SageMaker
HiveMindHiveMind
ClickworkerClickworker
Google Cloud Vertex AIGoogle Cloud Vertex AI

Top Subject Matter

Agriculture Domain Expertise
Soil Fertility
Pest Management

Top Data Types

TextText
DocumentDocument
ImageImage

Top Task Types

Fine Tuning
Question Answering
Bounding Box
Polygon
Classification
Entity Ner Classification
Object Detection
RLHF
Red Teaming
Text Summarization
Text Generation
Computer Programming Coding
Data Collection
Function Calling
Prompt Response Writing SFT
Evaluation Rating
Segmentation

Freelancer Overview

QwenMkulima: Agricultural LLM Fine-Tuning & AI Training. Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Bachelor of Science, Laikipia University (2023) and Certificate, DataCamp (2024). AI-training focus includes data types such as Text and Document and labeling workflows including Fine-tuning and Question Answering.

IntermediateEnglishSwahili

Labeling Experience

DOXA: RAG Pipeline for Document-Based QA Labeling & Extraction

DocumentQuestion Answering
Designed and implemented a RAG system to extract question-answer pairs from unstructured PDF documents using automated pipelines. Carried out document parsing, segmentation, and knowledge extraction methods to build a structured QA knowledge base. Developed context-preserving text chunking and vector search for efficient information retrieval. • Automated ingestion and parsing of multi-page agricultural and technical PDFs. • Labeled and organized extracted text into tailored chunked Q&A pairs for conversational AI queries. • Adopted FAISS for scalable vector-based document retrieval tasks. • Developed and tuned modular components for robust QA chain management.

Designed and implemented a RAG system to extract question-answer pairs from unstructured PDF documents using automated pipelines. Carried out document parsing, segmentation, and knowledge extraction methods to build a structured QA knowledge base. Developed context-preserving text chunking and vector search for efficient information retrieval. • Automated ingestion and parsing of multi-page agricultural and technical PDFs. • Labeled and organized extracted text into tailored chunked Q&A pairs for conversational AI queries. • Adopted FAISS for scalable vector-based document retrieval tasks. • Developed and tuned modular components for robust QA chain management.

2024 - Present

QwenMkulima: Agricultural LLM Fine-Tuning & AI Training

TextFine Tuning
Developed and deployed a domain-specific LLM by fine-tuning Qwen2.5-1.5B using QLoRA on over 6,000 agricultural instruction samples. Led the end-to-end pipeline for dataset preparation, fine-tuning, and evaluation to enhance factuality and reliability of model outputs in agricultural advisory tasks. Focused on improving domain recall, grounding, and structured response quality for the LLM through iterative training and validation. • Curated and cleaned large-scale instructional text data relevant to agricultural topics. • Engineered and executed the full fine-tuning workflow using PEFT, Transformers, and Accelerate. • Systematically evaluated and compared model responses versus base model outputs for factuality and domain accuracy. • Optimized model deployment for cost-effective inference on GPU hardware.

Developed and deployed a domain-specific LLM by fine-tuning Qwen2.5-1.5B using QLoRA on over 6,000 agricultural instruction samples. Led the end-to-end pipeline for dataset preparation, fine-tuning, and evaluation to enhance factuality and reliability of model outputs in agricultural advisory tasks. Focused on improving domain recall, grounding, and structured response quality for the LLM through iterative training and validation. • Curated and cleaned large-scale instructional text data relevant to agricultural topics. • Engineered and executed the full fine-tuning workflow using PEFT, Transformers, and Accelerate. • Systematically evaluated and compared model responses versus base model outputs for factuality and domain accuracy. • Optimized model deployment for cost-effective inference on GPU hardware.

2024 - Present

Education

D

DataCamp

Certificate, Data Science

Certificate
2024 - 2024
L

Laikipia University

Bachelor of Science, Computer Science

Bachelor of Science
2019 - 2023

Work History

R

Rhea Soil Management Limited

Lead Data Scientist

Nairobi
2024 - Present
V

Vunatec

Computer Vision Engineer

Makueni
2022 - 2024