Srijan Mani Tripathi

Key Skills

Software

Labelbox

Roboflow

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Document

Image

Text

Top Label Types

Bounding Box

Point Key Point

Entity Ner Classification

Segmentation

Freelancer Overview

I have hands-on experience in AI training data and data labeling projects involving text annotation, sentiment analysis, content moderation, and LLM response evaluation. I have worked with structured and unstructured datasets, ensuring high accuracy and consistency while following detailed annotation guidelines. My experience includes evaluating AI-generated responses for relevance, factual correctness, safety, and coherence, as well as tagging and categorizing data for machine learning model improvement. I am comfortable working with spreadsheets, annotation tools, and basic Python for data preprocessing and formatting tasks. What sets me apart is my strong analytical thinking, attention to detail, and ability to quickly understand complex instructions. I maintain high accuracy standards and deliver within deadlines, which is critical in AI training workflows. With a background in technology and problem-solving, I adapt quickly to new tools and project requirements, ensuring reliable and high-quality outputs for model development and evaluation tasks.

Labeling Experience

AI Training Data Annotation & LLM Evaluation Specialist

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Worked on AI training and data annotation projects involving text classification, Named Entity Recognition (NER), sentiment tagging, and LLM response evaluation. The scope included labeling structured and unstructured datasets to improve model accuracy, safety, and contextual understanding. Tasks involved rating AI-generated responses for factual correctness, relevance, bias detection, coherence, and policy compliance under RLHF frameworks. Handled datasets ranging from 5,000+ to 20,000+ entries per project while maintaining 95%+ accuracy standards. Followed strict annotation guidelines and participated in quality review cycles, including cross-validation and feedback-based corrections. Utilized tools like Labelbox, CVAT, and spreadsheet-based tracking systems to ensure consistency, timely delivery, and high-quality outputs suitable for fine-tuning and model optimization.

Worked on AI training and data annotation projects involving text classification, Named Entity Recognition (NER), sentiment tagging, and LLM response evaluation. The scope included labeling structured and unstructured datasets to improve model accuracy, safety, and contextual understanding. Tasks involved rating AI-generated responses for factual correctness, relevance, bias detection, coherence, and policy compliance under RLHF frameworks. Handled datasets ranging from 5,000+ to 20,000+ entries per project while maintaining 95%+ accuracy standards. Followed strict annotation guidelines and participated in quality review cycles, including cross-validation and feedback-based corrections. Utilized tools like Labelbox, CVAT, and spreadsheet-based tracking systems to ensure consistency, timely delivery, and high-quality outputs suitable for fine-tuning and model optimization.

2025

Education

Work History