Embedding Models internships for B.Tech students in Delhi

What students actually build

• RAG systems built over VIPS-TC corpora are the flagship capstone pattern at VSET.
• Choosing and evaluating an embedding model is a real decision in every student RAG build.

How VSET teaches Embedding Models

An embedding model maps text, images or other data into a dense vector where distance corresponds to meaning. Every semantic search, RAG and recommendation system starts by choosing and evaluating one. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & DS).

• Embeddings are documented in VSET's AI curriculum at learn.engineering.vips.edu alongside vector databases and RAG.
• They are taught with LangChain and LlamaIndex, the frameworks students use to build retrieval pipelines.
• The dimensionality-reduction and unsupervised material provides the geometric intuition behind them.
• Delivered inside the B.Tech CSE (AI & DS) track, one of VSET's seven GGSIPU-affiliated B.Tech programmes.

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 Embedding Models skills lead

Graduates applying Embedding Models skills typically target roles such as Search / Retrieval Engineer, AI Engineer, Data Engineer, NLP Engineer, LLM 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.