Jeelan Basha Shaik Mahammed

Key Skills

Software

Appen

Clickworker

Data Annotation Tech

Google Cloud Vertex AI

iMerit

Labelbox

Toloka

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Image

Top Label Types

Computer Programming Coding

Data Collection

Emotion Recognition

Prompt Response Writing SFT

Freelancer Overview

A seasoned data labeler experienced in AI training, I specialize in making good quality datasets for projects connected with Natural Language Processing, Computer Vision and IoT. I have a history of delivering relevant contributions in different projects, including text annotation for an advanced chatbot, image, and video labeling for self-driving car systems, and data preparation for activity recognition in IoT applications. I use industry-standard annotation tools in different projects but pay adequate attention to ensuring proper and efficient labeling of data in accordance with guidelines defined in the project so that the client requirements are met. I love working with data to ensure quality data for enhanced AI model performance. Collaborative work with multidisciplinary teams also reflects my passion in this field. Details and quick adaptability for productiveness are fields in which my effectiveness is witnessed in developing results on AI training data projects across different business sectors.

Labeling Experience

High-Precision Image Annotation for Self-Driving Cars

LabelboxImageBounding BoxPolygon

Labeled and annotated over 50,000 images to train object detection models for autonomous vehicles. Tasks included creating precise bounding boxes and polygons to identify vehicles, pedestrians, traffic signs, and road markings. Worked collaboratively with a QA team to ensure annotation quality exceeded 98% accuracy. Followed strict project guidelines for annotation consistency and conducted iterative reviews to align datasets with real-world driving scenarios.

Labeled and annotated over 50,000 images to train object detection models for autonomous vehicles. Tasks included creating precise bounding boxes and polygons to identify vehicles, pedestrians, traffic signs, and road markings. Worked collaboratively with a QA team to ensure annotation quality exceeded 98% accuracy. Followed strict project guidelines for annotation consistency and conducted iterative reviews to align datasets with real-world driving scenarios.

2023 - 2023

Education

Work History