For employers

Hire this AI Trainer

Sign in or create an account to invite AI Trainers to your job.

LoginCreate account
Invite to Job
Uri Fouzailov

Uri Fouzailov

Agency
ISRAEL flag
Tel aviv , Israel
www.ailabs.global/
$45.00/hrIntermediate120+SOC 2ISO 27001HIPPA

Key Skills

Software

LabelboxLabelbox
AppenAppen
V7 LabsV7 Labs
Surge AISurge AI
CVATCVAT
RoboflowRoboflow
SuperviselySupervisely
EncordEncord

Top Subject Matter

No subject matter listed

Top Data Types

TextText
DocumentDocument
Computer Code ProgrammingComputer Code Programming

Top Task Types

RLHF
Evaluation Rating
Text Generation
Computer Programming Coding
Classification

Company Overview

AI Labs is a specialized provider of high-quality data infrastructure and automation solutions for AI-driven companies. We focus on LLM training, human feedback systems (RLHF), and enterprise-grade data operations. By combining expert workforce with advanced tools, we enable organizations to improve model performance, reduce operational costs, and accelerate AI deployment across industries.

IntermediateHindiArabicFrenchGermanPolishEnglishRussianSpanish

Security

Security Overview

We implement enterprise-grade security and data protection practices across all operations. Our workflows are designed to ensure strict confidentiality of client data through role-based access controls, encrypted data handling (in transit and at rest), and secure work environments. We follow industry best practices aligned with SOC 2 and ISO 27001 frameworks, including workforce training, audit logging, and multi-layer quality assurance processes to ensure data integrity and traceability. For sensitive and high-security projects, we support restricted access environments, workforce segmentation under NDA, and deployment within private or on-premise infrastructures when required. For healthcare-related use cases, we support HIPAA-aligned workflows and strict data handling procedures. Our security approach is built to meet the needs of enterprise and AI-native companies, ensuring compliance readiness, data protection, and operational reliability at scale.

Security Credentials

SOC 2ISO 27001HIPPA

Labeling Experience

CVAT

Automotive Data Labeling for Computer Vision Models

CVATImageBounding BoxClassification
Performed image annotation for automotive computer vision models, including object detection and classification tasks. The project involved labeling vehicles, road elements, and traffic-related objects using bounding boxes and classification techniques. Implemented basic quality control processes to ensure consistency and accuracy across datasets, supporting model training and validation workflows.

Performed image annotation for automotive computer vision models, including object detection and classification tasks. The project involved labeling vehicles, road elements, and traffic-related objects using bounding boxes and classification techniques. Implemented basic quality control processes to ensure consistency and accuracy across datasets, supporting model training and validation workflows.

Present
Labelbox

LLM Training & RLHF Dataset Development for Enterprise AI Assistant

LabelboxTextRLHFEvaluation Rating
Delivered a high-quality RLHF dataset for training and evaluation of an enterprise AI assistant focused on business workflows and knowledge automation. The project included prompt-response generation, preference ranking, and multi-step reasoning evaluation tasks. The team developed detailed annotation guidelines and implemented multi-layer quality assurance processes, including gold standard tasks and inter-annotator agreement tracking. The dataset supported model fine-tuning and evaluation, improving response accuracy, reasoning quality, and consistency across real-world enterprise use cases.

Delivered a high-quality RLHF dataset for training and evaluation of an enterprise AI assistant focused on business workflows and knowledge automation. The project included prompt-response generation, preference ranking, and multi-step reasoning evaluation tasks. The team developed detailed annotation guidelines and implemented multi-layer quality assurance processes, including gold standard tasks and inter-annotator agreement tracking. The dataset supported model fine-tuning and evaluation, improving response accuracy, reasoning quality, and consistency across real-world enterprise use cases.

Present