Attention Mechanisms internships for B.Tech students in Delhi

What students actually build

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

How VSET teaches Attention Mechanisms

An attention mechanism lets a model weight every other position in a sequence when computing a representation for one position, instead of passing information along step by step. Self-attention is the single idea the transformer is built from. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).

• Attention and self-attention are explicitly covered in the transformer material published at learn.engineering.vips.edu.
• The curriculum teaches attention immediately after recurrent models, so the problem it solves is concrete.
• It is the foundation for the LLM, RAG and fine-tuning topics that follow in the same curriculum.
• Delivered inside the B.Tech CSE (AI & ML) track, one of VSET's seven GGSIPU-affiliated B.Tech programmes.

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 Attention Mechanisms skills lead

Graduates applying Attention Mechanisms skills typically target roles such as Machine Learning Engineer, LLM Engineer, Deep Learning Engineer, NLP Engineer, AI Research Associate. Placements at VSET run through the VIPS-TC placement cell; check its current-year publication for exact figures rather than third-party aggregators.