Pratik Khade

Key Skills

Software

Labelbox

Label Studio

SuperAnnotate

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Image

Text

Video

Top Label Types

Action Recognition

Bounding Box

Classification

Computer Programming Coding

Data Collection

Emotion Recognition

Evaluation Rating

Freelancer Overview

I am a B.Tech AI/ML graduate with hands-on experience in data annotation, LLM output evaluation, and NLP-focused AI projects. In my role as a Data Annotator at Innodata, I have accurately labeled and validated hundreds of data instances across NLP and multimodal datasets, working within enterprise-level AI data pipelines and adhering to strict quality standards. My freelance work includes evaluating over 2,000 LLM responses for accuracy and relevance, directly supporting prompt improvements and model refinement. I am skilled in Python, basic SQL, and prompt analysis, and have built practical applications such as an AI-powered PDF Q&A tool and a public transport app. I am passionate about delivering high-quality training data and contributing to robust, production-ready AI systems.

Labeling Experience

Text Sentiment Annotation for AI Model Training (Project)

Label StudioTextClassification

Created and annotated a sentiment dataset for NLP model training focused on opinion and sentiment detection. Categorized over 10,000 social media posts into Positive, Negative, or Neutral labels using specialized software. Ensured data cleaning and quality review to support accurate AI sentiment models. • Used Label Studio, Excel, and Python for efficient annotation. • Performed data preprocessing and ensured annotation accuracy. • Conducted quality reviews for model readiness. • Facilitated training data creation for NLP research.

Created and annotated a sentiment dataset for NLP model training focused on opinion and sentiment detection. Categorized over 10,000 social media posts into Positive, Negative, or Neutral labels using specialized software. Ensured data cleaning and quality review to support accurate AI sentiment models. • Used Label Studio, Excel, and Python for efficient annotation. • Performed data preprocessing and ensured annotation accuracy. • Conducted quality reviews for model readiness. • Facilitated training data creation for NLP research.

2025 - 2025

Sentiment Analyst – CrowdGen

Label StudioTextClassification

Worked as Sentiment Analyst to analyze and classify sentiment in online text content using manual annotation and NLP tools. Labeled content to enhance the quality and performance of sentiment analysis models. Generated reports and ensured annotation consistency for accurate AI model results. • Categorized social media and online posts as Positive, Negative, or Neutral. • Collaborated with review cycles to refine data accuracy. • Used Label Studio, Excel, and Python for annotation processes. • Produced and communicated sentiment trends for business use.

Worked as Sentiment Analyst to analyze and classify sentiment in online text content using manual annotation and NLP tools. Labeled content to enhance the quality and performance of sentiment analysis models. Generated reports and ensured annotation consistency for accurate AI model results. • Categorized social media and online posts as Positive, Negative, or Neutral. • Collaborated with review cycles to refine data accuracy. • Used Label Studio, Excel, and Python for annotation processes. • Produced and communicated sentiment trends for business use.

2025 - 2025

Content Moderation & NSFW Detection System (Project)

SuperannotateImageClassification

Developed a dataset for content moderation and NSFW detection AI systems by annotating thousands of images and texts. Labeled samples as Safe, Borderline, or Explicit according to defined policies using advanced annotation tools. Ensured data quality and compliance with content review guidelines. • Utilized SuperAnnotate, Prodigy, and Label Studio for annotation tasks. • Followed detailed moderation policies and QA processes. • Structured dataset formats with CSV/JSON. • Supported AI model training for unsafe materials detection.

Developed a dataset for content moderation and NSFW detection AI systems by annotating thousands of images and texts. Labeled samples as Safe, Borderline, or Explicit according to defined policies using advanced annotation tools. Ensured data quality and compliance with content review guidelines. • Utilized SuperAnnotate, Prodigy, and Label Studio for annotation tasks. • Followed detailed moderation policies and QA processes. • Structured dataset formats with CSV/JSON. • Supported AI model training for unsafe materials detection.

2024 - 2024

Data Annotation Specialist – SOUL AI

LabelboxImageClassification

Served as Data Annotation Specialist, annotating diverse datasets including images and reviewing NSFW content for AI model training. Used Labelbox for labeling, following strict privacy and ethical guidelines. Enhanced consistency and accuracy, collaborating with QA to correct annotation errors. • Image and text datasets were categorized for machine learning use. • Labeled NSFW and sensitive content according to set policies. • Maintained annotation documentation and improvement feedback. • Supported data quality for robust AI models.

Served as Data Annotation Specialist, annotating diverse datasets including images and reviewing NSFW content for AI model training. Used Labelbox for labeling, following strict privacy and ethical guidelines. Enhanced consistency and accuracy, collaborating with QA to correct annotation errors. • Image and text datasets were categorized for machine learning use. • Labeled NSFW and sensitive content according to set policies. • Maintained annotation documentation and improvement feedback. • Supported data quality for robust AI models.

2024 - 2024

Education

Work History