Alexander Seelam - AI Data Specialist – Multimodal, Healthcare & FinTech

Key Skills

Software

Appen

CVAT

Labelbox

LabelImg

V7 Labs

Surge AI

Roboflow

Encord

Supervisely

Argilla

Sama

Hasty

iMerit

Tagtog

Toloka

Doccano

Datature

Data Annotation Tech

OneForma

Telus

Mindrift

Internal/Proprietary Tooling

Mercor

Top Subject Matter

Healthcare – Medical Imaging & Diagnostics Data

Finance – Risk Analysis, KYC & Fraud Detection

Artificial Intelligence – Data Annotation, RLHF & Model Evaluation

Top Data Types

Computer Code Programming

Medical Dicom

Document

Top Task Types

Entity Ner Classification

Data Collection

Red Teaming

Evaluation Rating

RLHF

Company Overview

2une is a data infrastructure company focused on building high-quality datasets and evaluation pipelines for next-generation AI systems. Our mission is to accelerate the development of reliable, scalable, and safe AI by solving one of its hardest problems: access to clean, diverse, and production-grade data. We provide end-to-end services including data collection, annotation, RLHF (Reinforcement Learning with Human Feedback), model evaluation, and benchmarking. Our workflows are optimized for speed and accuracy through proprietary quality-control layers, multi-stage validation, and domain-specific annotation protocols. 2une operates a rapidly growing workforce of 8,000+ trained annotators, including students and domain specialists across India. We specialize in high-impact industries such as healthcare (medical imaging, diagnostics datasets), fintech (risk, KYC, lending data), and multimodal AI (vision, voice, and text). Our unique strength lies in combining scalable human intelligence with structured pipelines—enabling faster iteration cycles for AI teams. We also support custom dataset generation (e.g., voice, medical, behavioral data) tailored to specific model requirements. Founded by experienced builders with backgrounds in startups, research, and large-scale product deployments, 2une has collaborated with global AI companies and emerging startups. We prioritize data security and compliance, implementing strict access controls and anonymization protocols. With a strong community-driven workforce and a focus on quality at scale, 2une is positioned as a trusted partner for organizations building cutting-edge AI systems.

ExpertEnglishHindi

Security

Security Overview

At 2une, security and privacy are foundational to how we design, collect, and process data for AI systems. We implement strict data governance practices to ensure that all datasets are handled responsibly, securely, and in compliance with global standards. All data is processed within controlled environments with role-based access controls (RBAC), ensuring that only authorized personnel can access sensitive information. We follow the principle of least privilege across our systems and enforce secure authentication mechanisms. We apply end-to-end data protection measures, including encryption in transit and at rest, anonymization, and de-identification—especially for sensitive domains like healthcare and financial data. Personally identifiable information (PII) is either removed or masked based on project requirements. Our annotation workflows include multi-layer quality checks and audit trails, enabling full traceability of data handling and modifications. We also maintain strict guidelines for annotators, including NDAs, compliance training, and monitored work environments. 2une is committed to aligning with industry best practices and regulatory frameworks such as GDPR-equivalent standards and emerging AI safety guidelines. We continuously improve our security infrastructure to support enterprise-grade AI deployments. By combining robust technical safeguards with disciplined operational processes, 2une ensures that client data remains secure, private, and trustworthy at every stage of the AI lifecycle.

Security Credentials

ISO 27001SOC 2HIPPA

Labeling Experience

Medical Imaging Annotation for AI Diagnostics

ImageSegmentation

Led a large-scale medical imaging annotation project involving CT scans and X-rays for training diagnostic AI models. Tasks included organ segmentation, lesion detection, and CAC scoring alignment across gated and non-gated scans. Managed a team of trained annotators working under expert-reviewed protocols. Processed over 50,000+ medical images with multi-layer QA, including double-blind reviews and expert validation to ensure clinical-grade accuracy.

2026 - Present

RLHF Dataset Creation for LLM Evaluation

TextRLHF

Designed and executed RLHF pipelines for evaluating LLM responses across reasoning, safety, and instruction-following tasks. Annotators ranked model outputs, provided feedback, and created high-quality reference responses. Generated 500,000+ evaluation samples with strict quality rubrics and inter-annotator agreement checks.

2025 - 2026

Voice Dataset Collection & Transcription for Conversational AI

AudioData Collection

Built and managed a scalable voice dataset pipeline across multiple Indian languages. Tasks included audio collection, transcription, accent tagging, and noise classification for training ASR models. Handled 100,000+ audio samples with structured validation layers, including automated duplicate detection and human QA checks.

2025 - 2025