Neha Zite

Key Skills

Software

Appen

Data Annotation Tech

Labelbox

Mercor

Telus

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Video

Top Task Types

Audio Recording

Classification

Evaluation Rating

Prompt Response Writing SFT

Translation Localization

Freelancer Overview

As a highly-skilled Data Annotation and Labeling Specialist, I create and refine high-quality datasets that are essential for training robust Al and machine learning models. I possess deep understanding of various annotation techniques and tools, enabling me to accurately and efficiently label a wide spectrum of data types, including images, text, audio, and video. My work is focused on ensuring the integrity and consistency of data, providing the foundational layer for effective model development and deployment. With a strong focus on project guidelines and quality control, I have a proven track record of delivering clean and reliable data that directly contributes to improved model performance.

Labeling Experience

LLM Evaluation in Marathi, English & Hindi

Data Annotation TechTextText SummarizationTranslation Localization

Train and evaluate AI models through prompt engineering, response assessment and rubric creation, conduct English-Marathi bilingual translation and localization tasks, review and rate AI outputs and peer work to ensure accuracy, linguistics quality and consistency.

Train and evaluate AI models through prompt engineering, response assessment and rubric creation, conduct English-Marathi bilingual translation and localization tasks, review and rate AI outputs and peer work to ensure accuracy, linguistics quality and consistency.

2025 - 2025

Data Annotation

TelusImageObject Detection

Specialize in detailed image annotation and dataset labelling for computer vision projects, ensure accuracy and consistency in large-scale image datasets, contribute to creating high-quality training data for AI and adapt to diverse annotation guidelines across domains.

Specialize in detailed image annotation and dataset labelling for computer vision projects, ensure accuracy and consistency in large-scale image datasets, contribute to creating high-quality training data for AI and adapt to diverse annotation guidelines across domains.

2025

Education

Work History