AI Intern

AboutKaromi

ManageArtworks(A flagship product brought to you byKaromi, a leading Enterprise SaaS provider) enables 4000 leading Global and Indian brands. We offer everything to get artwork projects going & manage every step of the packaging and artwork process. Companies reach markets faster with our end-end packaging & artwork management system while achieving 100% compliance.

Project Duration:

Location:

Overview

AI-powered packaging artwork design platform

Key Deliverables

• Design and implement an

  AI-driven artwork layout engine

  capable of interpreting textual and visual input todetermineplacement of design elements.
• Build

  LLM-based modules

  that understand packaging copy content, regulatory zones, and design intent.
• Develop

  rule-based and heuristic models

  for spacing, hierarchy, and alignment of packaging components (e.g., ingredients, logos, nutrition tables, symbols).
• Integrate

  vision and layout analysis models

  (e.g.,LayoutLM, Detectron2, or similar architectures) to map artwork panels and guide composition.
• Combine

  text-to-layout reasoning

  (LLM) with

  visual placement algorithms

  for adaptive artwork generation.
• Implement and integrate the layout engine into existing

  Flask/FastAPI

  microservices for seamless deployment in production.
• Build evaluation pipelines for layout quality, visual balance, and compliance metrics.
• Document model architecture, placement logic, heuristics, and integration strategy for both technical and product teams.

Desired Candidate Profile

Must-Have Skills

• Strong programming skills in

  Python

• Knowledge of

  LLMs

  and

  prompt engineering

  for reasoning tasks
• Experience in

  Computer Vision

  and

  Natural Language Processing (NLP)

• Experience with

  rule-based heuristics

  or constraint-based design optimization
• Understanding of

  packaging artwork workflows

  and regulated content placement (food, pharma, or FMCG)
• Proficiencyin

  PyTorch/TensorFlow

  for custom model development
• Exposure to

  Flask/FastAPI

  integration and API-based architectures
• Experience with

  vector/geometry libraries

  (e.g., Shapely, OpenCV) for layout computation
• Strong debugging and rapid prototyping capabilities

Good-to-Have Skills

• Familiarity with

  Generative AI for design automation

  (text-to-image or layout synthesis)
• Background in

  graphic design principles

  , typography, and visual hierarchy
• Knowledge of

  SVG/PDF parsing andrendering

  for artwork files

Outcome

• A functional

  AI-powered layout automation engine

  capable of generating packaging artworks with minimal manual intervention
• Integration of

  LLM reasoning + heuristic rules + vision models

  for intelligent design placement
• Improved

  design turnaround time

  and

  regulatory compliance consistency

• Technical proof-of-concepts for

  automated artwork design and layout generation

• Recommendations for

  scaling the system into a full design automation product