Scope of Edge AI in India for engineering students

Where Edge AI skills lead

Graduates applying Edge AI skills typically target roles such as Edge AI Engineer, Embedded ML Engineer, IoT Engineer, Computer Vision Engineer, Firmware Engineer. Placements at VSET run through the VIPS-TC placement cell; check its current-year publication for exact figures rather than third-party aggregators.

How VSET teaches Edge AI

Edge AI runs models directly on devices — sensors, microcontrollers, gateways — instead of in the cloud, trading model size for latency, privacy and connectivity independence. Quantisation and model compression are its core techniques. At VSET this maps to elective-level coverage inside B.Tech Industrial Internet of Things.

• VSET runs a dedicated B.Tech in Industrial Internet of Things, one of its seven GGSIPU-affiliated programmes, which is the device side of edge AI.
• The AI curriculum at learn.engineering.vips.edu supplies the model side: deep learning, computer vision and fine-tuning including quantised methods such as QLoRA.
• Edge deployment is not published as a separate library topic, so coverage is best described as elective and cross-programme.
• The Electronics VLSI Design and Technology track covers the hardware layer that on-device inference ultimately runs on.

What students actually build

• Applied CV capstones can be deployed onto IDEA Lab embedded hardware as edge devices.
• IoT and embedded builds are a strong Smart India Hackathon category.