u/Useful-Wallaby-5874

I'd recommend incoming students, as well as those in their UG/PG programs to take a careful look at this Stanford Emerging Technology Review document to have a clear picture about the future of technology, and the opportunities available in different fields.

This might help you move from the software engineering craze to be able to see the bigger picture where expertise in domains like materials science, biotech, quantum and semiconductors, will be of much more value than only software engineering skills. Also to be noted is the emphasis on how professionals in almost all STEM fields need to be proficient in AI (not just using AI tools, but be able to build them).

https://preview.redd.it/4te8vid8qb2h1.png?width=1364&format=png&auto=webp&s=9eab688e9e4ce39fccc98d95603e5aff5aeeac89

Not trying to offer unsolicited advice, but as someone who has seen a bit of the world and how AI is transforming the future of work, here are a few thoughts:

1. Aim for higher studies after graduation: The next wave of in-demand professionals will be those with strong domain expertise (at least at the Master’s level, if not a PhD) combined with knowledge of AI. So, try to maintain a strong CGPA (CGPA matters, irrespective of what you're told by peers or seniors) and consider applying to PhD programs abroad (with full funding/scholarship) in your field of interest (STEM fields). Many of the consulting roles where top B-School alumni opt for, are being fast replaced by AI, so I'm not sure if pursuing MBA is the right choice now (except from the top schools).
2. Regardless of your department, learn AI on your own. It is not very difficult, you just need knowledge of basic Python programming, probability and statistics, some calculus, and, most importantly, be curious about solving problems. The ability to ask the right questions and continuously learn new concepts is key.
3. Do not treat materials science/metallurgy or biotechnology as "lower branches": These are fields of the future, where rapid progress is essential for the advancement of humanity, and there are many unanswered questions. Jobs involving repetitive work (e.g., basic to mid-level software engineering roles) are already declining, while fields with unsolved problems are set to grow significantly, with the integration of AI and computations.
4. If you’re studying CS, treat it as an applied mathematics/applied physics field, not just for software engineering or coding. I’ve seen many accomplished computer scientists who are not great coders, but they are awesome mathematicians and problem solvers. Coding alone is no longer a skill, but you need to know enough to ask, understand and evaluate what AI models generate. Instead focus on having a strong foundation in algorithms, recent hardware (GPUs, new architectures from Cerebras, Groq, etc), and coding for specific hardware, and AI/quantum computing. Of course, if you are an exceptional coder who can heavily optimize production systems, you will still be in high demand.

Try to use the NIT tag to explore opportunities at top universities around the world, particularly in the US and Europe (preferably with full scholarship), rather than settling for a job immediately after graduation. Do not focus only on salary packages after your BTech, learn to aim higher, and stand out among the hundreds of thousands of engineers graduating every year. You have the potential to change your own life and your family’s future, as well as make an impact on the world.

Most of the recent posts in this group have been about packages and complaints or rants about seniors, peers, and professors, so I thought of introducing something different. I hope this interests you and encourages you to ask more questions here about becoming the engineers and scientists of the future, instead of focusing on trivial things.

Happy to answer any questions you may have, so please feel free to post them publicly for everyone to see.

The submitted NIRF2026 data from NIT Rourkela mentions a median salary of INR 18.37 LPA for undergraduates who entered the college in the 2021-22 academic year.

https://preview.redd.it/qntuhpu25ayg1.png?width=2432&format=png&auto=webp&s=abffd88b669fe8b7ab70da61dbbeee676265600f

However, the placement statistics webpage of the institute mentions that the median is actually 10.5 LPA and the average is ~14 LPA, which are very different from the 18.37 LPA median claimed in the NIRF data (even if you account for the differences in the dates at which these data were collected, the difference is just too large 18.37 vs 10.5 LPA). So, which exactly is the correct number?

https://preview.redd.it/upqtra836ayg1.png?width=2464&format=png&auto=webp&s=ee3495ee194f369002f8a1b6e0acb702d72976b0

https://preview.redd.it/30ea7eo56ayg1.png?width=2498&format=png&auto=webp&s=2d648433369176f7e6b6334afa2c8e56c5b49eb9