Job Description

Education Preference- Only B.tech CS/IT Graduate from the batch of 2025/2024

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Key Responsibilities:

Collect and curate large-scale botanical image datasets, including scraping from online sources and organizing local image repositories with proper structure and naming conventions. Manually annotate plant images with precision (leaf, flower, fruit, stem, etc.) using tools like CVAT, LabelImg, or Labelme, while ensuring consistency and reviewing for quality. Assist in the pre-processing of image data (e.g., resizing, filtering, normalization, and augmentation) and monitor automated pipelines for correctness and completeness. Help validate annotations and perform sanity checks through basic machine learning inference (e.g., verifying predictions from classification or segmentation models). Collaborate closely with ML teams to flag edge cases, improve annotation guidelines, and maintain high data hygiene throughout the pipeline.

Requirements:

Prior experience handling large-scale image datasets (ideally in the terabyte range) and a strong understanding of digital image formats and metadata handling. Knowledge of plant/botanical structures and ability to visually distinguish between different parts (e.g., leaf vs. flower), preferably with academic or project exposure. Proficiency in basic Python scripting -- familiarity with os, pandas, opencv, Pillow and ability to automate repetitive data handling tasks. Understanding of image annotation workflows and tools (CVAT, LabelImg, Labelme), plus familiarity with pre-processing and augmentation techniques commonly used in computer vision. Basic exposure to machine learning concepts such as image classification, segmentation, and inference, along with the discipline to carry out repetitive annotation tasks with high accuracy.
Job Types: Full-time, Internship
Contract length: 3 months

Pay: ?7,000.00 per month

Benefits:

Paid sick time Paid time off
Schedule:

Day shift Fixed shift Monday to Friday
Location:

Noida, Uttar Pradesh (Required)
Work Location: In person

Application Deadline: 26/07/2025