Gautam Kumar

Key Skills

Software

CVAT

Roboflow

Labelbox

Appen

Figure Eight

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Video

Top Label Types

Bounding Box

Polygon

Segmentation

Evaluation Rating

Prompt Response Writing SFT

Emotion Recognition

Freelancer Overview

I am a detail-oriented data annotator and AI data analyst with hands-on experience in creating and validating high-quality training data for AI systems. My work centers on executing precise, step-by-step GUI-based workflows, primarily within Ubuntu and Linux desktop environments, using tools like LibreOffice, GIMP, and VS Code. I excel at following complex annotation guidelines, maintaining consistency, and performing manual QA to ensure reliable and accurate data for model improvement. My background also includes multilingual content review and translation, which enhances my attention to detail and ability to handle repetitive, guideline-driven tasks with patience and accuracy. I am well-suited for non-coding, desktop-based annotation projects and am committed to supporting the development of robust AI models through meticulous data handling.

Labeling Experience

AI/Data Training, Data Science & Analytics, Translation & Admin Support.

AppenImageBounding BoxPolygon

Annotated sports images by creating precise bounding boxes around actively used volleyballs during rally situations. Ensured accurate labeling of fast-moving and motion-blurred objects while following detailed annotation guidelines using the CVAT platform. Delivered consistent, high-quality annotations to support computer vision models for sports analytics.

Annotated sports images by creating precise bounding boxes around actively used volleyballs during rally situations. Ensured accurate labeling of fast-moving and motion-blurred objects while following detailed annotation guidelines using the CVAT platform. Delivered consistent, high-quality annotations to support computer vision models for sports analytics.

2024

Fireweed project

AppenTextEmotion RecognitionEvaluation Rating

**Fireweed Project – Short Description** The Fireweed project (Appen) focused on training and improving Large Language Models (LLMs) by generating high-quality prompts, evaluating AI responses, and rating outputs based on accuracy, relevance, and safety guidelines. The role involved fact-checking, identifying misinformation, scoring model performance, and providing structured feedback to enhance multilingual AI systems.

**Fireweed Project – Short Description** The Fireweed project (Appen) focused on training and improving Large Language Models (LLMs) by generating high-quality prompts, evaluating AI responses, and rating outputs based on accuracy, relevance, and safety guidelines. The role involved fact-checking, identifying misinformation, scoring model performance, and providing structured feedback to enhance multilingual AI systems.

2024 - 2025

Education

Work History