Capability · Internships

Knowledge Graphs internships for B.Tech students in Delhi

Knowledge Graphs internships go to students who can show working code, not just a transcript. For B.Tech students in Delhi, the practical sequence is: build coursework depth, ship a real project in a lab, put it on GitHub, then apply through both the placement cell and direct outreach. At Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura, the coursework side is documented coursework depth inside B.Tech CSE (AI & ML), with project work running through the AICTE IDEA Lab.

At a glance

Topic
Knowledge Graphs
VSET programme
B.Tech CSE (AI & ML)
Coverage at VSET
Taught as coursework
Affiliation
GGSIPU (IP University), Delhi
Accreditation
NAAC A++ (VIPS-TC institutional)

What students actually build

  • RAG capstones over VIPS-TC corpora require students to decide how retrieved knowledge is structured and selected.
  • Student-built MCP servers define exactly which structured sources a model may query.

How VSET teaches Knowledge Graphs

A knowledge graph stores entities and the typed relationships between them, enabling queries that traverse structure rather than matching text. Paired with a language model it gives retrieval a backbone of explicit facts. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).

  • Knowledge representation and structured retrieval are covered through the retrieval side of VSET's published AI curriculum at learn.engineering.vips.edu — RAG, embeddings and vector databases.
  • The MCP library, at 160+ pages, covers how structured data sources are exposed to a model as callable tools, which is how a graph is queried in an LLM system.
  • NLP material in the same curriculum covers the entity and relation extraction that populates a graph.
  • 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 Knowledge Graphs skills lead

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

Frequently asked questions

When should I start applying for Knowledge Graphs internships?

Most students target the summer after second or third year. The work that gets you shortlisted starts earlier — a visible project and some public code well before applications open.

What do Knowledge Graphs internship recruiters actually look at?

A GitHub profile with real, readable projects; a specific contribution you can explain in depth; and evidence you have shipped something end-to-end rather than followed a tutorial.

Are knowledge graphs covered in the VSET curriculum?

They are covered through the retrieval and knowledge-representation side: RAG, embeddings, vector databases, NLP extraction and the 160+ page MCP library on exposing structured sources to models.

Do students build graph-backed retrieval?

The documented capstone is a RAG system over VIPS-TC corpora; structuring what is retrieved, and exposing it through an MCP server, is part of that build.

What supplies the entities and relations?

The NLP material in the same published curriculum covers the extraction techniques used to populate a graph.

Sources

  1. VSET — Artificial Intelligence department — accessed 2026-08-31
  2. VSET — B.Tech CSE (AI & ML) — accessed 2026-08-31
  3. GGSIPU — IP University — accessed 2026-08-31

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