Research-heavy ML projects vs. deployed GenAI projects for IIT campus placements

I’m an M.Tech student at old IIT from one of the top branch targeting ML Engineer / Data ML roles.

While preparing my CV, I’ve started feeling a bit behind after seeing my friends’ projects. Many of them have deployed RAG applications, Hybrid RAG pipelines, and similar GenAI projects that can now be built relatively easily using tools like Claude.

My projects are more research-oriented. I’ve worked on RAG, but my research focuses on understanding how tokenizer vocabulary, embeddings, and encoding affect retrieval and generation. I also have a paper under review at an A* publication venue.

My other projects are also research-heavy, involving multimodal ML, cross-attention, and agentic AI.

For recruiters, hiring managers, and people who have recently gone through IIT placement drives:

- What do companies actually prioritize for ML / Data ML roles?

- Are they mainly looking for deployed GenAI projects and buzzwords?

- Do they still value deep ML understanding and architectural knowledge?

- Is a research-heavy CV a disadvantage compared to a product/deployment-focused CV?