Anmol K.

Key Skills

Software

Other

OpenCV AI Kit (OAK)

Top Subject Matter

Geospatial address validation and QA testing

Computer vision for number plate detection and geolocation-based enrichment

Top Data Types

Image

Top Task Types

Geocoding

Object Detection

Freelancer Overview

Software Testing Intern (Beans.ai) - geospatial data accuracy and address validation support. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other and OpenCV AI Kit (OAK). Education includes Bachelor of Technology in Computer Science, Shree Ram Swaroop Memorial University. AI-training focus includes data types such as Geospatial, Tiled Imagery, and Image and labeling workflows including Geocoding and Object Detection.

Labeling Experience

Engineering Intern (DriveU) - vehicle/number plate detection model development

OpenCV AI Kit (OAK)ImageObject Detection

During the Engineering Intern role, Anmol Kumar developed an object detection model for vehicle number plates using YOLOv8 and custom data augmentation. The task required preparing/using training image data to enable accurate identification of vehicle plates. He also integrated geolocation-based mapping for contextual data enrichment used for real-time safety and routing features. • Developed a number plate detection model using YOLOv8 with custom data augmentation. • Used model development workflows that depend on annotated visual data for detection. • Mapped driver locations to nearby weather stations using geolocation and APIs for real-time context. • Collaborated on applied AI features supporting safety and route planning.

2024 - 2024

Software Testing Intern (Beans.ai) - geospatial data accuracy and address validation support

OtherGeocoding

During the Software Testing Intern role, Anmol Kumar worked on enhancing geospatial data accuracy for address validation using ArcGIS-based analysis workflows. This involved verifying and improving the correctness of address-related information to support downstream mapping and QA checks. The work functioned as part of preparing/validating geospatial inputs used for data-driven location analysis. • Enhanced address validation for data accuracy using ArcGIS analysis. • Built address testing workflows in Figma to support QA validation steps. • Collaborated with the QA team to identify and resolve issues affecting address/location data quality. • Optimized testing processes for more reliable geospatial datasets.

2023 - 2023

Education

Work History