Careers after B.Tech with Document AI skills

Where Document AI skills lead

Graduates applying Document AI skills typically target roles such as NLP Engineer, AI Engineer, Machine Learning Engineer, Search / Retrieval 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.

What students actually build

• The flagship VSET capstone — RAG systems built over VIPS-TC corpora — is a document-understanding system end to end.
• Applied NLP tools are a documented capstone category covering extraction and classification work.

How VSET teaches Document AI

Document AI extracts structure and meaning from unstructured documents — parsing, layout understanding, entity extraction, classification and question answering over PDFs and scans. It combines OCR-style vision with language modelling. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).

• The core techniques are documented at learn.engineering.vips.edu: NLP, computer vision, RAG, embeddings and vector databases.
• RAG is taught explicitly as the technique for answering questions over a document corpus, which is the dominant document-AI pattern.
• The MCP library covers how document stores are exposed to a model as callable tools.
• Taught inside the GGSIPU-affiliated B.Tech CSE (AI & ML) track.