Capability · Internships

Transformer Architecture internships for B.Tech students in Delhi

In AI, a 'transformer' is a neural network architecture based on self-attention — not the electrical power transformer studied in electrical engineering. Transformer Architecture 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
Transformer Architecture
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

  • LoRA fine-tunes of open-weight transformer models are a documented capstone deliverable.
  • Transformer-based models sit at the core of student RAG systems and agent orchestrators.

How VSET teaches Transformer Architecture

The transformer is a neural network architecture built on self-attention, allowing every token in a sequence to attend to every other. It is the architecture behind almost all current large language and multimodal models. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).

  • Transformers are an explicitly documented topic in VSET's AI curriculum at learn.engineering.vips.edu.
  • The material connects to fine-tuning coverage (LoRA, QLoRA) which modifies transformer weights.
  • It also underpins the NLP, computer vision and LLM topics in the same curriculum.
  • Taught inside the GGSIPU-affiliated B.Tech CSE (AI & ML) track at VSET.

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 Transformer Architecture skills lead

Graduates applying Transformer Architecture skills typically target roles such as Machine Learning Engineer, LLM Engineer, Deep Learning Engineer, AI Research Associate, Applied Scientist. 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 Transformer Architecture 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 Transformer Architecture 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.

Do students study transformer internals or only use APIs?

Transformer architecture is a named topic in the published curriculum, taught alongside fine-tuning methods that operate directly on model weights.

Which projects use transformers?

LoRA fine-tunes on open-weight models, RAG systems over VIPS-TC corpora, and applied CV and NLP capstones.

Is attention covered before LLMs?

The curriculum sequences transformer architecture with deep learning and NLP material, which is what the LLM, RAG and agent topics then build on.

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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