Kalyuug K.

Key Skills

Software

Label Studio

Top Subject Matter

Computer Vision / Object Detection

Computer Vision / Semantic Segmentation

NLP / Conversational AI Training Data

Top Data Types

Image

Text

Document

Top Task Types

Bounding Box

Segmentation

Classification

Freelancer Overview

AI Data Annotation & Training Data Specialist — Image Annotation & QA (Freelance/Self-Driven). Core strengths include Label Studio and Google Sheets. Education includes Bachelor of Technology, MIT ADT University. AI-training focus includes data types such as Image, Text, and Document and labeling workflows including Bounding Box, Segmentation, and Entity (NER).

Labeling Experience

AI Data Annotation & Training Data Specialist — Dataset Cleaning & Validation

DocumentClassification

Cleaned, validated, and organized structured datasets used for AI training and evaluation workflows. Ensured label and record integrity by removing duplicates and applying normalization and verification steps. Supported consistent downstream training by flagging errors and maintaining organized dataset batches. • Deduplication, normalization, and error-flagging • Structured dataset cleaning and validation • Verification aligned with ML pipeline standards • Preparation of organized data for training datasets

2023 - Present

AI Data Annotation & Training Data Specialist — NLP Text Tagging (Freelance/Self-Driven)

Label StudioText

Executed NLP text tagging tasks to create structured labels for conversational AI and chatbot training datasets. Labeled named entities and performed intent and sentiment categorization to support downstream model learning. Maintained dataset quality through QA validation and error-flagging to meet accuracy benchmarks. • Named entity recognition (NER) tagging • Intent classification for conversational AI • Sentiment analysis labeling • QA reviews and error-rate reduction

2023 - Present

AI Data Annotation & Training Data Specialist — Segmentation (Freelance/Self-Driven)

Label StudioImageSegmentation

Performed semantic segmentation annotation on image datasets to support training of computer vision models. Ensured label consistency and compliance with industry-standard ML dataset pipeline formats. Used QA checks to reduce annotation errors and improve dataset reliability for model training. • Semantic segmentation labeling • Data validation and consistency checks • QA-driven reduction of labeling errors • Preparation of structured outputs for ML pipelines

2023 - Present

AI Data Annotation & Training Data Specialist — Image Annotation & QA (Freelance/Self-Driven)

Label StudioImageBounding Box

Annotated thousands of images for computer vision tasks, including object detection and bounding box labeling, aligned with ML data pipeline requirements. Performed consistent annotation quality checks to maintain accuracy across large dataset batches. Contributed to reliable training data by validating and organizing outputs for downstream model use. • Object detection labeling with bounding boxes • Data quality assurance (QA) and error-rate reduction • Dataset cleaning, validation, and deduplication • Preprocessing/formatting aligned with ML pipelines

2023 - Present

Education

Work History