Nithin V.

Key Skills

Software

CrowdSource

Data Annotation Tech

Google Cloud Vertex AI

Mindrift

OpenCV AI Kit (OAK)

Remotasks

Scale AI

Toloka

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Image

Text

Top Task Types

Computer Programming/Coding

Data Collection

Fine-tuning

Object Detection

Translation/Localization

Freelancer Overview

With over 3 years of hands-on experience in data labeling and AI training data workflows, I specialize in creating high-quality, structured datasets essential for training computer vision, natural language processing (NLP), and machine learning models. I’ve worked extensively with tools like Labelbox, CVAT, SuperAnnotate, and custom annotation platforms to annotate images, videos, and text. My contributions have directly supported the development of cutting-edge AI models in industries including healthcare, e-commerce, and autonomous systems. What sets me apart is my strong attention to detail, understanding of AI model behavior, and ability to maintain annotation consistency across large datasets. I’ve collaborated with ML engineers and data scientists to ensure that datasets meet project requirements and quality benchmarks. My background also includes experience with quality assurance, edge case handling, ontology management, and feedback loops for continuous data improvement.

Labeling Experience

AI trainer

Scale AIComputer Code ProgrammingText GenerationEmotion Recognition

Worked on multiple large-scale AI training data projects involving image, video, and text annotation for machine learning model development. Key projects included annotating medical images for diagnostic AI systems, labeling street-level imagery for autonomous vehicle perception, and tagging e-commerce product data for recommendation and search algorithms. Responsible for using tools such as Labelbox, CVAT, and custom-built platforms to perform bounding box, polygon, keypoint, semantic segmentation, and text classification annotations. Collaborated closely with machine learning engineers to define annotation guidelines, resolve ambiguities, and ensure high-quality labeled datasets through continuous quality control and feedback loops. Managed edge cases, improved data consistency, and contributed to ontology creation and refinement for various AI domains. Played a critical role in accelerating model training cycles by delivering accurate, well-structured, and scalable labeled data .

2022 - 2024

Education

Work History