Conversational AI internships for B.Tech students in Delhi

What students actually build

• RAG systems over VIPS-TC corpora — the flagship VSET capstone — are grounded conversational systems in practice.
• Student-built MCP servers and LangGraph orchestrators add the tool-calling and control layers such systems need.

How VSET teaches Conversational AI

Conversational AI builds systems that hold multi-turn dialogue with users — managing state, grounding answers in data, calling tools and handling escalation. Modern implementations are language models wrapped in retrieval, memory and control logic. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).

• Every layer is documented in VSET's published curriculum at learn.engineering.vips.edu: LLMs, prompt engineering, RAG, vector databases and agent frameworks.
• The 160+ page MCP library covers how a conversational system reaches tools and data sources.
• LangChain, LangGraph, CrewAI and AutoGen are all covered, which is the orchestration layer behind multi-turn control flow.
• 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 Conversational AI skills lead

Graduates applying Conversational AI skills typically target roles such as LLM Application Developer, Conversational AI Engineer, NLP Engineer, AI Engineer, Conversation Designer. Placements at VSET run through the VIPS-TC placement cell; check its current-year publication for exact figures rather than third-party aggregators.