Large Language Models internships for B.Tech students in Delhi

What students actually build

• Students fine-tune open-weight models with LoRA as part of the capstone pattern.
• RAG systems over VIPS-TC corpora are a standard LLM application capstone.

How VSET teaches Large Language Models

Large language models are transformer-based neural networks trained on very large text corpora to predict and generate language. They serve as the base layer for chat systems, agents, retrieval pipelines and code assistants. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).

• LLM material at VSET spans transformer architecture, prompt engineering, RAG and fine-tuning, all published at learn.engineering.vips.edu.
• Fine-tuning coverage includes LoRA and QLoRA on open-weight models.
• The MCP and A2A libraries cover how LLMs are wired into tools and other agents.
• Delivered inside the B.Tech CSE (AI & ML) track, a GGSIPU-affiliated programme.

How students find them

Two channels, used together: the VIPS-TC placement cell, which coordinates campus internship drives, and direct outreach — applying to startups and labs with a specific project to point at. Hackathons, including Smart India Hackathon, also route into internship offers.

Where Large Language Models skills lead

Graduates applying Large Language Models skills typically target roles such as LLM Engineer, AI Engineer, NLP Engineer, Applied Scientist, AI Application Developer. Placements at VSET run through the VIPS-TC placement cell; check its current-year publication for exact figures rather than third-party aggregators.