Careers after B.Tech with AI in Finance skills

Where AI in Finance skills lead

Graduates applying AI in Finance skills typically target roles such as Data Scientist, Machine Learning Engineer, Quantitative Analyst, Risk Modelling Analyst, 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.

What students actually build

• Financial framings are a common student choice within the applied ML and RAG capstone categories.
• Smart India Hackathon problem statements have a recurring financial-services strand that student teams enter.

How VSET teaches AI in Finance

AI in finance covers credit scoring, risk modelling, algorithmic trading signals, document processing and forecasting over financial time series. It leans heavily on tabular machine learning, time-series methods and, more recently, language models over filings and reports. At VSET this maps to elective-level coverage inside B.Tech CSE (AI & Data Science).

• The techniques are taught: the B.Tech CSE (AI & Data Science) track covers machine learning, deep learning and the data pipeline side, with the wider AI library published at learn.engineering.vips.edu.
• Finance is not a named domain subject at VSET — it appears as a project domain rather than as taught vertical coursework.
• RAG and NLP material in the same curriculum is what students use when working over filings, statements and policy documents.
• AI & DS is one of VSET's seven GGSIPU-affiliated B.Tech programmes.