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Illipilli James

@illipillijames

Data annotator on AYA Data, Kreme, and Tengun projects; labeled fiber, tracked vehicles, and segmented LiDAR point clouds.

India

Message

On AYA Data, Kreme, and Tengun projects, I annotated fiber structures, tracked vehicles across video frames, labeled road infrastructure, and segmented LiDAR point clouds. I followed project guidelines and checked labels for consistency, completeness, and accuracy.

I developed a vehicle identification and surveillance system using Python, OpenCV, and YOLOv8, and built an interactive aptitude and reasoning application. My internship experience included prompt engineering and exposure to generative AI and machine learning workflows.

Experience

Work history, roles, and key accomplishments

AI & ML Virtual Intern

ExcelR

Aug 2025 - Oct 2025 (2 months)

Learned machine learning workflows, AI model fundamentals, and practical concepts behind predictive and data-driven systems.

Machine Learning AIFundamentals

ChatGPT, Prompt Engineering & Generative AI Intern

Black Bucks

May 2025 - Jul 2025 (2 months)

Created structured prompts for technical and business use cases; gained exposure to Generative AI applications, automation concepts, and documentation.

Prompt Engineering Generative AI Documentation

Data Annotator

Kreme

Tracked and annotated vehicles across video frames while maintaining consistent object identities. Labeled road signs, traffic lights, pillars, and other visible road infrastructure for computer vision datasets.

Video Annotation Object Tracking Vehicle Tracking Road Object Annotation Traffic Sign Recognition Traffic Signals Pillar Labeling Quality Assurance

Data Annotator

AYA Data

Annotated fiber structures by identifying and labeling relevant features according to project-specific instructions. Followed annotation guidelines and checked labels for consistency, completeness, and accuracy.

Data Annotation Fiber Annotation Quality Assurance Annotation Guidelines

Data Annotator

Tengun

Performed segmentation and labeling of objects in LiDAR point cloud data according to task requirements. Applied project-specific class definitions and quality guidelines to support structured 3D perception datasets.

3D Point Cloud Segmentation 3D Data Annotation Data Annotation Quality Assurance