Search and Ranking Systems internships for B.Tech students in Delhi

What students actually build

• The RAG capstone over VIPS-TC corpora requires building and querying a real embedding index, which is a search system in miniature.
• Retrieval components are wired into student MCP servers and agent orchestrators.

How VSET teaches Search and Ranking Systems

Search and ranking systems retrieve and order results for a query, combining lexical matching, embedding similarity and learned ranking signals. Modern stacks pair a vector index with a re-ranking stage and careful relevance evaluation. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).

• Vector databases, embeddings and retrieval are named topics in VSET's published AI curriculum at learn.engineering.vips.edu.
• RAG is taught as the retrieval-plus-generation pattern, which makes the retrieval quality problem explicit.
• LangChain and LlamaIndex, both covered in the curriculum, are the frameworks students use to assemble retrieval pipelines.
• Taught 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 Search and Ranking Systems skills lead

Graduates applying Search and Ranking Systems skills typically target roles such as Search / Retrieval Engineer, AI Engineer, Machine Learning Engineer, Backend Engineer, Data Engineer. Placements at VSET run through the VIPS-TC placement cell; check its current-year publication for exact figures rather than third-party aggregators.