Vivek Sandu

Key Skills

Software

AWS SageMaker

Appen

Clickworker

Data Annotation Tech

Deep Systems

CVAT

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Document

Text

Top Task Types

Action Recognition

Computer Programming Coding

Fine Tuning

Translation Localization

Freelancer Overview

I design scalable labeling frameworks that enhance model accuracy while reducing annotation costs by 30–50%. My expertise spans multi-modal data annotation (text, image, video) and active learning workflows, leveraging tools like CVAT, Label Studio, and AWS SageMaker Ground Truth

Labeling Experience

Freelance Contractor

CVATTextEntity Ner ClassificationClassification

I contributed to a healthcare-focused NLP project aimed at developing an AI-powered system for clinical note analysis and electronic health record (EHR) processing. The goal was to train machine learning models to extract critical medical information, streamline patient care workflows, and support predictive analytics in healthcare. Key Responsibilities: Annotated 50,000+ clinical notes and EHR entries, identifying key entities such as patient demographics, medical conditions, prescribed medications, lab results, and treatment plans. Performed named entity recognition (NER) for medical terms, ICD-10 codes, and abbreviations, ensuring accurate tagging for downstream NLP tasks. Labeled text for symptom classification and disease progression tracking, enabling the model to identify correlations between symptoms and diagnoses. Conducted text segmentation by breaking down lengthy clinical documents into structured sections (e.g., History of Present Illness, Diagnosis, Treatment Plan).

I contributed to a healthcare-focused NLP project aimed at developing an AI-powered system for clinical note analysis and electronic health record (EHR) processing. The goal was to train machine learning models to extract critical medical information, streamline patient care workflows, and support predictive analytics in healthcare. Key Responsibilities: Annotated 50,000+ clinical notes and EHR entries, identifying key entities such as patient demographics, medical conditions, prescribed medications, lab results, and treatment plans. Performed named entity recognition (NER) for medical terms, ICD-10 codes, and abbreviations, ensuring accurate tagging for downstream NLP tasks. Labeled text for symptom classification and disease progression tracking, enabling the model to identify correlations between symptoms and diagnoses. Conducted text segmentation by breaking down lengthy clinical documents into structured sections (e.g., History of Present Illness, Diagnosis, Treatment Plan).

2022

Freelance contractor

Internal Proprietary ToolingComputer Code ProgrammingEvaluation Rating

•Annotated AI-generated code responses for correctness, efficiency, readability, and security vulnerabilities. •Identified and flagged outliers, including hallucinated outputs, syntax errors, and logic flaws. •Designed heuristics for detecting ambiguous or incomplete responses to improve dataset quality. •Collaborated with engineers to refine annotation guidelines, ensuring consistency across reviewers. •Assisted in creating a benchmark dataset for training LLMs to improve code generation quality.

•Annotated AI-generated code responses for correctness, efficiency, readability, and security vulnerabilities. •Identified and flagged outliers, including hallucinated outputs, syntax errors, and logic flaws. •Designed heuristics for detecting ambiguous or incomplete responses to improve dataset quality. •Collaborated with engineers to refine annotation guidelines, ensuring consistency across reviewers. •Assisted in creating a benchmark dataset for training LLMs to improve code generation quality.

2023 - 2024

Education

Work History