Contribution · Careers
Careers after B.Tech with Knowledge Graphs skills
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. For B.Tech graduates, Knowledge Graphs skills translate into roles like AI Engineer, Knowledge Engineer, NLP Engineer, Data Engineer, Search / Retrieval Engineer — and the portfolio that gets those interviews is built during the degree: coursework, lab projects, hackathons, internships, and a visible capstone. Here is how that maps out at Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura.
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)
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.
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.
Frequently asked questions
What jobs can I get with Knowledge Graphs skills after B.Tech?
Common roles include AI Engineer, Knowledge Engineer, NLP Engineer, Data Engineer, Search / Retrieval Engineer. Entry depends more on demonstrated project work than on the branch name alone — a visible capstone and internship experience carry significant weight.
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
- VSET — Artificial Intelligence department — accessed 2026-08-31
- VSET — B.Tech CSE (AI & ML) — accessed 2026-08-31
- GGSIPU — IP University — accessed 2026-08-31