Ojasvi Gupta

Key Skills

Software

Appen

CrowdSource

Data Annotation Tech

Dataloop

Labelbox

Prodigy

Roboflow

Scale AI

SuperAnnotate

Top Subject Matter

Healthcare and Natural Language Processing (NLP)

Hindi, English , Bengali, Arabian, and Thai

Healthcare (Medical Video and Scribing) and NLP Siri, Alexa, ChatGPT

Top Data Types

Audio

Computer Code Programming

Video

Top Task Types

Computer Programming Coding

Data Collection

Prompt Response Writing SFT

RLHF

Translation Localization

Freelancer Overview

Results-driven professional with a PhD in Computer Science (2022) and over 7.8 years of experience in data labeling and AI training data, specializing in delivering high-quality annotated datasets across various domains. Recipient of the National Award in Data Labeling AI and the Best AI Trainer Award - 2022, along with the Excellence in Multilingual Research Award - 2023, showcasing exceptional expertise in training data creation for complex AI systems. Skilled in leveraging advanced tools like Ango Hub, SuperAnnotate, V7, Kili, Supervisely, Dataloop, Labelbox, SegmentsAI, Encord, Scale AI, Amazon SageMaker Ground Truth, and Prodigy to ensure precise, scalable, and efficient data annotation workflows. Experienced in leading large-scale labeling projects for organizations such as Capgemini and Invisible Technologies, with a proven track record of driving innovation in multilingual NLP datasets, computer vision applications, and healthcare AI. Adept at building and managing cross-functional teams, optimizing annotation pipelines, and delivering results that meet client requirements and industry standards. Recognized for a deep understanding of AI model requirements and a commitment to advancing the quality of AI training data in a rapidly evolving field.

Labeling Experience

Multilingual Text Classification and Sentiment Analysis for AI-Powered Customer Support

Scale AITextPrompt Response Writing SFT

This project was large-scale, involving millions of customer queries from diverse sources. Quality control measures, such as double annotation and feedback loops, were employed to maintain the integrity and accuracy of the labels. Continuous refinement of the AI model based on user feedback ensured that the AI assistant could provide highly relevant and empathetic responses.

This project was large-scale, involving millions of customer queries from diverse sources. Quality control measures, such as double annotation and feedback loops, were employed to maintain the integrity and accuracy of the labels. Continuous refinement of the AI model based on user feedback ensured that the AI assistant could provide highly relevant and empathetic responses.

2022 - 2023

Education

Work History