Semantic Search 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.
• Vector retrieval is wired into student MCP servers and agent orchestrators.

How VSET teaches Semantic Search

Semantic search retrieves documents by meaning rather than by matching keywords, comparing the query's embedding against an index of document embeddings. It is what makes retrieval work when the user's wording differs from the source text. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & DS).

• Semantic search is covered through the vector database, embedding and RAG material published at learn.engineering.vips.edu.
• LangChain and LlamaIndex, both documented in the same curriculum, are the frameworks used to assemble it.
• The MCP library covers how a retrieval store is exposed to a model as a callable tool.
• 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 Semantic Search skills lead

Graduates applying Semantic Search skills typically target roles such as Search / Retrieval Engineer, AI Engineer, Data Engineer, Backend 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.