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Nischal Karthikumar

Nischal Karthikumar

AI Training Data Specialist

USA flagPlainsboro, Usa
$25.00/hrEntry Level

Key Skills

Software

No software listed

Top Subject Matter

Artificial Intelligence – Machine Learning & Model Training
Data Annotation – Text, Audio & Multimodal Labeling
Media Evaluation – Audio, Video, and Content Quality Assessment

Top Data Types

TextText
ImageImage
VideoVideo

Top Task Types

Text GenerationText Generation
Evaluation/RatingEvaluation/Rating
Prompt + Response Writing (SFT)Prompt + Response Writing (SFT)
TranscriptionTranscription
Question AnsweringQuestion Answering
Object DetectionObject Detection
Data CollectionData Collection
Fine-tuningFine-tuning
Text SummarizationText Summarization

Freelancer Overview

I have experience working in AI training data across multiple modalities, including text labeling, prompt-response writing for supervised fine-tuning (SFT), and quality evaluation tasks. I have labeled and reviewed large volumes of text data for classification tasks, generated prompt-response pairs, and assessed model outputs for accuracy and relevance. In addition, I have evaluated and compared AI-generated videos and audio outputs, providing ratings based on quality, coherence, and adherence to given criteria. I also have experience transcribing and translating audio from different languages into accurate text, ensuring clarity and correctness in meaning. Across all projects, I developed strong attention to detail, consistency in following evaluation guidelines, and the ability to make structured judgments across ambiguous or subjective data. This experience strengthened my understanding of multimodal AI systems and how high-quality human feedback improves model training and performance.

Entry LevelEnglishGermanTamilHindiTelugu

Labeling Experience

Project Ohm

TextPrompt Response Writing SFT
Project Ohm focused on structured text annotation to improve machine learning model performance in natural language understanding tasks. Responsibilities included labeling and categorizing text data based on sentiment, intent, topic relevance, and contextual meaning according to strict annotation guidelines. The project involved processing a large volume of text samples, ensuring accuracy, consistency, and adherence to detailed labeling rules. I performed quality checks on my annotations and refined decisions based on feedback to maintain high dataset reliability. The work contributed to building cleaner, more accurate training data for AI model development.

Project Ohm focused on structured text annotation to improve machine learning model performance in natural language understanding tasks. Responsibilities included labeling and categorizing text data based on sentiment, intent, topic relevance, and contextual meaning according to strict annotation guidelines. The project involved processing a large volume of text samples, ensuring accuracy, consistency, and adherence to detailed labeling rules. I performed quality checks on my annotations and refined decisions based on feedback to maintain high dataset reliability. The work contributed to building cleaner, more accurate training data for AI model development.

2026 - Present

Project HedgeHog

ImageEvaluation Rating
Project HedgeHog involved large-scale text labeling and annotation tasks to support the development of machine learning models. The work focused on classifying and tagging textual data based on sentiment, intent, relevance, and predefined category schemas. I reviewed and labeled thousands of text samples with high attention to consistency, accuracy, and adherence to detailed annotation guidelines. The dataset included user-generated text inputs requiring careful interpretation to ensure correct labeling across multiple categories. Quality was maintained through regular review checks, guideline compliance verification, and iterative feedback loops to improve labeling precision. This helped ensure the final dataset met the standards required for training reliable AI models.

Project HedgeHog involved large-scale text labeling and annotation tasks to support the development of machine learning models. The work focused on classifying and tagging textual data based on sentiment, intent, relevance, and predefined category schemas. I reviewed and labeled thousands of text samples with high attention to consistency, accuracy, and adherence to detailed annotation guidelines. The dataset included user-generated text inputs requiring careful interpretation to ensure correct labeling across multiple categories. Quality was maintained through regular review checks, guideline compliance verification, and iterative feedback loops to improve labeling precision. This helped ensure the final dataset met the standards required for training reliable AI models.

2026 - Present

Education

P

Purdue University

Bachelor of Science, Pharmaceutical Science

Bachelor of Science
2025 - 2029
W

West Windsor Plainsboro High School South

High School Diploma, General Education

High School Diploma
2021 - 2025

Work History

B

Beta Chi Theta

PR and Finance Chair

West Lafayette
2025 - Present
P

Purdue Biological Sciences

Undergraduate Research Assistant

West Lafayette
2025 - Present