Model Evaluation and Benchmarking internships for B.Tech students in Delhi

What students actually build

• Evaluation and guardrail design form part of the RAG, agent and fine-tuning capstones students build.
• Hackathon builds, including Smart India Hackathon entries, are judged on working behaviour, which forces measurement under time pressure.

How VSET teaches Model Evaluation and Benchmarking

Model evaluation measures whether a system actually works — through held-out metrics, task-specific benchmarks, human review and, for language models, LLM-as-judge and adversarial testing. It is the discipline that separates a demo from a deployable system. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).

• Evaluation runs through VSET's published AI curriculum at learn.engineering.vips.edu, and is treated directly in the AI safety material.
• The curriculum's systems orientation — RAG, agents, MCP — forces evaluation as an engineering step rather than an afterthought.
• The fine-tuning material on LoRA and QLoRA requires measuring whether adaptation actually improved behaviour.
• 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 Model Evaluation and Benchmarking skills lead

Graduates applying Model Evaluation and Benchmarking skills typically target roles such as ML Engineer (Evaluation), AI Safety Engineer, Machine Learning Engineer, Applied Scientist, AI Engineer. Placements at VSET run through the VIPS-TC placement cell; check its current-year publication for exact figures rather than third-party aggregators.