Geoffrey Munene

Key Skills

Software

AWS SageMaker

Anno-Mage

Appen

Argilla

Axiom AI

Clickworker

CloudFactory

CrowdFlower

CrowdSource

CVAT

Data Annotation Tech

Dataloop

Datatroniq

Datasaur

Deep Systems

Diffgram

Doccano

Figure Eight

Google Cloud Vertex AI

HiveMind

Humanatic

Img Lab

Kili Technology

Labelbox

LabelImg

Label Studio

Lionbridge

Mighty AI

OneForma

OpenCV AI Kit (OAK)

Redbrick AI

Remotasks

Sama

Toloka

Telus

Trilldata Technologies

VoTT

V7 Labs

Internal/Proprietary Tooling

Scale AI

Prodigy

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Text

Video

Top Task Types

Bounding Box

Classification

Fine Tuning

Prompt Response Writing SFT

RLHF

Freelancer Overview

I specialize in AI training data, data annotation, and model optimization, leveraging structured labeling, reinforcement learning, and statistical modeling to enhance machine learning accuracy. My work has contributed to 15%+ improvements in AI model performance, refining datasets and optimizing workflows. At Invisible Technologies, I developed high-quality datasets, improving contextual relevance by 25% and streamlining processes for greater efficiency. Beyond structured annotation, I have contributed to NLP projects for global clients, working on text classification, sentiment analysis, and conversational AI fine-tuning. My background in economics, statistics, and predictive modeling enables me to bridge AI development with mathematical precision, ensuring data-driven solutions for AI-enhanced decision-making. With hands-on experience across Python, SQL, Power BI, Tableau, and Stata, I efficiently handle large-scale structured datasets, uncovering insights to improve AI model reliability. Additionally, I have expertise in computer vision, image & video annotation, and autonomous system labeling, contributing to AI applications in self-driving technology, finance analytics, and retail automation. My strong foundation in game theory, probability models, and numerical optimization allows me to fine-tune AI models for efficiency, accuracy, and ethical AI development.

Labeling Experience

AI Training & Data Annotation for LLM Optimization

ProdigyTextEntity Ner ClassificationRLHF

Worked on large-scale data annotation and LLM optimization for a leading AI company, refining text, image, and video datasets to enhance machine learning accuracy. Text Data: Conducted entity recognition, sentiment analysis, and classification to fine-tune natural language processing (NLP) models, increasing response accuracy by 25%. Image & Video Data: Labeled bounding boxes, object detection, and tracking datasets for autonomous systems and AI-enhanced retail automation, improving AI visual recognition efficiency by 20%. Quality Assurance: Implemented reinforcement learning and structured dataset fine-tuning, reducing error rates by 30% and improving contextual relevance. Project Size: Processed millions of labeled instances, collaborating with global AI teams to ensure high-quality, structured datasets for LLM and computer vision applications.

Worked on large-scale data annotation and LLM optimization for a leading AI company, refining text, image, and video datasets to enhance machine learning accuracy. Text Data: Conducted entity recognition, sentiment analysis, and classification to fine-tune natural language processing (NLP) models, increasing response accuracy by 25%. Image & Video Data: Labeled bounding boxes, object detection, and tracking datasets for autonomous systems and AI-enhanced retail automation, improving AI visual recognition efficiency by 20%. Quality Assurance: Implemented reinforcement learning and structured dataset fine-tuning, reducing error rates by 30% and improving contextual relevance. Project Size: Processed millions of labeled instances, collaborating with global AI teams to ensure high-quality, structured datasets for LLM and computer vision applications.

2022 - 2024

Education

Work History