LLM Fine-Tuning internships for B.Tech students in Delhi

What students actually build

• LoRA fine-tunes on open-weight models are a named part of the VSET capstone pattern.
• Fine-tuned models are often combined with student RAG systems and agent orchestrators.

How VSET teaches LLM Fine-Tuning

Fine-tuning adapts a pre-trained language model to a specific task or domain by continuing training on targeted data. Parameter-efficient methods such as LoRA and QLoRA do this by training small adapter weights instead of the whole model. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).

• Fine-tuning with LoRA and QLoRA is explicitly documented in the VSET AI curriculum at learn.engineering.vips.edu.
• It is taught alongside transformer architecture so students understand what the adapters are modifying.
• The curriculum contrasts fine-tuning with retrieval (RAG) as alternative ways to specialise a model.
• Delivered inside the GGSIPU-affiliated B.Tech CSE (AI & ML) track.

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 LLM Fine-Tuning skills lead

Graduates applying LLM Fine-Tuning skills typically target roles such as LLM Engineer, Machine Learning Engineer, AI Engineer, Applied Scientist, MLOps Engineer. Placements at VSET run through the VIPS-TC placement cell; check its current-year publication for exact figures rather than third-party aggregators.