Dennis Kariri

Key Skills

Software

AWS SageMaker

Appen

Clickworker

CloudFactory

CrowdFlower

CrowdSource

CVAT

Data Annotation Tech

Figure Eight

Hasty

HiveMind

iMerit

Labelbox

LabelImg

Label Studio

Mindrift

OneForma

Remotasks

Roboflow

Scale AI

SuperAnnotate

Toloka

Top Subject Matter

AI Model Training & Data Annotation for AI Systems

Large Language Model (LLM) Evaluation and NLP

AI Model Evaluation for Large Language Models (LLMs)

Top Data Types

Audio

Image

Video

Top Task Types

Bounding Box

Classification

Object Detection

Polygon

Prompt Response Writing SFT

Freelancer Overview

An experienced Data Annotation Specialist and AI Trainer with over three years of expertise in training and evaluating AI systems. Proficient in prompt engineering, text annotation, sentiment analysis, and multilingual data labeling in English, French, and German. I have worked on diverse projects, including refining large language models (LLMs), conducting image and audio annotation, and testing AI outputs for accuracy and usability. My background includes creating high-quality datasets for machine learning, providing actionable feedback for AI improvements, and collaborating with cross-functional teams to optimize workflows. Known for attention to detail, I excel at enhancing AI systems' contextual understanding, ensuring precise model evaluations, and delivering accurate data for various AI training tasks.

Labeling Experience

Segmentation Task for AI Model Training

CVATImageSegmentation

In this project, I completed a segmentation task using the CVAT platform, annotating images with labels such as Construction, Tree, Plant, Lawn, Solid Surface, Water Surface, Short Barrier, Item, Sky, Earth, and Undefined. I used the Brush tool for precise segmentation and followed detailed guidelines to ensure high-quality annotations, such as avoiding objects behind glass and using the "Remove underlying pixels" function. This process minimized overlap and improved the quality of labeled data. The outcome was a comprehensive dataset that significantly enhanced the accuracy and efficiency of AI model training for machine learning applications.

In this project, I completed a segmentation task using the CVAT platform, annotating images with labels such as Construction, Tree, Plant, Lawn, Solid Surface, Water Surface, Short Barrier, Item, Sky, Earth, and Undefined. I used the Brush tool for precise segmentation and followed detailed guidelines to ensure high-quality annotations, such as avoiding objects behind glass and using the "Remove underlying pixels" function. This process minimized overlap and improved the quality of labeled data. The outcome was a comprehensive dataset that significantly enhanced the accuracy and efficiency of AI model training for machine learning applications.

2024 - 2024

3D and 2D Annotation with Bounding Box for AI Model Training

Scale AIImageBounding BoxPolygon

As a Data Annotation Specialist at Remotasks from 2022 to January 2024, I worked on annotating 3D sensor and 2D image data using bounding boxes for object detection tasks. The projects involved creating high-quality labels for vehicles, pedestrians, road signs, and other objects to train AI systems for autonomous vehicles. My tasks included precise placement of bounding boxes in both 2D and 3D spaces, ensuring alignment with project guidelines and minimizing errors. I consistently delivered high-quality annotations by adhering to strict quality standards, reviewing data for accuracy, and collaborating with team members to streamline workflows. This work contributed to improving the accuracy and reliability of AI systems in detecting and classifying objects in real-world environments.

As a Data Annotation Specialist at Remotasks from 2022 to January 2024, I worked on annotating 3D sensor and 2D image data using bounding boxes for object detection tasks. The projects involved creating high-quality labels for vehicles, pedestrians, road signs, and other objects to train AI systems for autonomous vehicles. My tasks included precise placement of bounding boxes in both 2D and 3D spaces, ensuring alignment with project guidelines and minimizing errors. I consistently delivered high-quality annotations by adhering to strict quality standards, reviewing data for accuracy, and collaborating with team members to streamline workflows. This work contributed to improving the accuracy and reliability of AI systems in detecting and classifying objects in real-world environments.

2022 - 2024

Education

Work History