For employers

Hire this AI Trainer

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

LoginCreate account
Invite to Job
AITOMS

AITOMS

Agency
USA flagClackamas County, Oregon, United States, Usa
www.aitoms.ai
$36.00/hrIntermediate48+HIPPAGDPR

Key Skills

Software

AWS SageMakerAWS SageMaker
Data Annotation TechData Annotation Tech
LabelboxLabelbox
Internal/Proprietary Tooling
Scale AIScale AI
Label StudioLabel Studio

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code ProgrammingComputer Code Programming
Geospatial Tiled ImageryGeospatial Tiled Imagery
ImageImage

Top Task Types

Bounding Box
Computer Programming Coding
Data Collection
Object Detection
Prompt Response Writing SFT

Company Overview

AITOMS is an expert data annotation firm specializing in complex, high-dimensional datasets for pioneering AI. Our mission: "Where Data Meets Definition." We turn your most challenging data streams into structured, model-ready insights. We specialize in 3D LiDAR, volumetric medical imaging (MRI/CT), hyperspectral & SAR imagery, and complex audio signals. Our unique method involves domain experts (physicists, radiologists, etc.) working with our proprietary annotation tools to ensure unparalleled accuracy. We operate under strict security protocols to handle sensitive data for clients in our specialized industries. Our workforce is comprised of highly trained specialists who guarantee quality and confidentiality.

IntermediateHindiBengaliEnglishRussianSpanish

Security

Security Overview

AITOMS is committed to a security-first culture. Our comprehensive security program is built upon the principles of leading industry frameworks such as ISO 27001 and the NIST Cybersecurity Framework. For all projects involving protected health information (PHI), our processes are designed to be fully HIPAA-compliant. Physical Security: Our operational facilities are secured with 24/7 video surveillance and multi-factor access control, ensuring only authorized personnel can enter sensitive areas. We enforce a strict clean desk policy and prohibit personal electronic devices in the production environment to prevent unauthorized data transfer. Cybersecurity: Our network architecture is designed for defense-in-depth. We utilize enterprise-grade firewalls, endpoint detection and response (EDR) on all workstations, and mandate encrypted data transfer protocols (SFTP/TLS). Client data is always logically segregated in dedicated, secure cloud environments with strict access controls based on the principle of least privilege. Confidentiality & Data Handling: Every AITOMS team member undergoes a background check and is bound by a comprehensive non-disclosure agreement (NDA). We conduct mandatory, ongoing training on data privacy and security best practices. These sessions are documented and serve as proof of our commitment to maintaining a vigilant and well-informed workforce. Audits & Verification: We perform regular internal audits and vulnerability assessments to proactively identify and mitigate risks. We are prepared for client security reviews and maintain detailed documentation of our policies, procedures, and controls to demonstrate compliance and operational security.

Security Credentials

HIPPAGDPR

Labeling Experience

Label Studio

Enterprise LLM Fine-Tuning for Code Security and Optimization

Label StudioComputer Code ProgrammingClassificationEvaluation Rating
We are engaged by a leading enterprise client to create a high-fidelity dataset for fine-tuning their proprietary Large Language Model (LLM), which is designed for automated code security analysis and performance optimization. The project scope covers a corpus of over 50,000 code snippets from real-world repositories, focusing on Python (Django, Flask), Java (Spring Boot), and JavaScript/TypeScript (React, Node.js). Our team of senior DevSecOps engineers is performing multi-label classification of code against the OWASP Top 10 and CWE Top 25 vulnerability standards, identifying critical issues such as SQL Injection, Cross-Site Scripting (XSS), and insecure deserialization. Our methodology involves a comprehensive Supervised Fine-Tuning (SFT) strategy, where our experts author thousands of prompt-response pairs that transform vulnerable or inefficient code into a secure, optimized equivalent.

We are engaged by a leading enterprise client to create a high-fidelity dataset for fine-tuning their proprietary Large Language Model (LLM), which is designed for automated code security analysis and performance optimization. The project scope covers a corpus of over 50,000 code snippets from real-world repositories, focusing on Python (Django, Flask), Java (Spring Boot), and JavaScript/TypeScript (React, Node.js). Our team of senior DevSecOps engineers is performing multi-label classification of code against the OWASP Top 10 and CWE Top 25 vulnerability standards, identifying critical issues such as SQL Injection, Cross-Site Scripting (XSS), and insecure deserialization. Our methodology involves a comprehensive Supervised Fine-Tuning (SFT) strategy, where our experts author thousands of prompt-response pairs that transform vulnerable or inefficient code into a secure, optimized equivalent.

2024
Labelbox

Early-Stage Autism Screening via Behavioral Video Analysis

LabelboxVideoAction RecognitionClassification
Developed a comprehensive digital platform for the early detection of Autism Spectrum Disorder (ASD) and personalized therapy recommendations. The project utilizes a proprietary machine learning model to analyze behavioral videos of children captured by their parents. The model determines a percentage of autism, which works as a preliminary test. The platform requires user registration with NID verification to ensure child safety.

Developed a comprehensive digital platform for the early detection of Autism Spectrum Disorder (ASD) and personalized therapy recommendations. The project utilizes a proprietary machine learning model to analyze behavioral videos of children captured by their parents. The model determines a percentage of autism, which works as a preliminary test. The platform requires user registration with NID verification to ensure child safety.

2024
Labelbox

Road Object Detection for Autonomous Vehicles in Bangladesh

LabelboxImageBounding BoxClassification
Contributed to the "Bangladesh Road Object Detection for Autonomous Vehicles Challenge". The project involved working with a dataset of 189,825 images containing 78,943 objects across 13 distinct classes. The dataset featured a wide range of road types, including towns, expressways, and highways, to challenge algorithms across various driving contexts in Bangladesh. The task was to annotate objects with rectangular bounding boxes to train robust object detection models.

Contributed to the "Bangladesh Road Object Detection for Autonomous Vehicles Challenge". The project involved working with a dataset of 189,825 images containing 78,943 objects across 13 distinct classes. The dataset featured a wide range of road types, including towns, expressways, and highways, to challenge algorithms across various driving contexts in Bangladesh. The task was to annotate objects with rectangular bounding boxes to train robust object detection models.

2024 - 2024

Pre-Clinical Trial MRI Brain Tumor Segmentation

Internal Proprietary ToolingMedical DicomClassificationDiagnosis
Partnered with a leading biotech firm to perform precise 3D semantic segmentation of gliomas across 1,500 multi-sequence MRI scans. Our team of board-certified radiologists worked within our secure, HIPAA-compliant environment, using proprietary AI-assist tools to achieve a Dice score of 0.92, exceeding the client's requirements for their pre-clinical trial AI model.

Partnered with a leading biotech firm to perform precise 3D semantic segmentation of gliomas across 1,500 multi-sequence MRI scans. Our team of board-certified radiologists worked within our secure, HIPAA-compliant environment, using proprietary AI-assist tools to achieve a Dice score of 0.92, exceeding the client's requirements for their pre-clinical trial AI model.

2024 - 2024
AWS SageMaker

Acoustic Event Detection for Industrial Equipment Monitoring

Aws SagemakerAudioAudio RecordingClassification
For a client in the industrial manufacturing sector, we executed a pilot project to classify acoustic data from factory floor machinery to build a dataset for a predictive maintenance AI. Using AWS SageMaker Ground Truth, our team labeled thousands of short audio clips, classifying them into categories such as 'normal operation,' 'rotor imbalance,' and 'critical failure alarm.' We annotated the precise start and end times for each distinct acoustic event.

For a client in the industrial manufacturing sector, we executed a pilot project to classify acoustic data from factory floor machinery to build a dataset for a predictive maintenance AI. Using AWS SageMaker Ground Truth, our team labeled thousands of short audio clips, classifying them into categories such as 'normal operation,' 'rotor imbalance,' and 'critical failure alarm.' We annotated the precise start and end times for each distinct acoustic event.

2023 - 2024