Machine Learning / AI Engineering Internship (LLM Focus)

This is a remote position.

This internship project focuses on continuing the development of an LLM+VLM tool that supports both multi modal content and external enterprise data sources.

The objective is to give users a unified way to ask questions, analyze documents, and build retrieval workflows, whether the data comes from local files, images, scanned documents, and external sources.

The intern(s) will join an existing team to continue the framework that allows the platform to securely connect to external systems.

This includes support for authentication, configuration, connection health checks, source discovery, and document ingestion into existing analysis and RAG pipelines.

The project will emphasize clean architecture so new connectors can be added later with minimal changes to the core platform.

Attention will be given to multi-user and multi-organization requirements such as role-based access control, token-based access, and separation of data between organizations and users.

Because some tasks may take time to complete, the internship will integrate a job management model for long-running operations such as batch analysis, ingestion, and indexing. This includes thinking about queue visibility, task status, and scalable execution in a multi-user environment.

This project is well-suited for a student interested in AI systems, backend development, API design, multi modal AI, and secure enterprise integration.

A strong understanding of distributed and concurrent systems, Docker, Python, REST, LLM/VLM API usage, and on-premises infrastructure is expected of candidates.

By the end of the internship, the students will present the features and abilities added to corporate leaders.

Requirements

Fields/ Disciplines Used: 

• Artificial intelligence (AI) fundamentals 
  
• Multimodal AI / vision-language models (VLMs) 
  
• Large language models (LLMs) 
  
• Computer vision concepts 
  
• Retrieval-augmented generation (RAG) 
  
• Backend software engineering 
  
• API design and integration 
  
• Distributed and concurrent systems 
  
• Network interoperability 
  
• Enterprise systems integration 
  
• Identity and access management (RBAC) 
  
• Cloud and on-prem infrastructure 
  
• Containerization using Docker
  

Benefits