Episode 89
How to Survive the AI Wave as an Engineer (IIT Kharagpur Grad POV) - w/ Aayush
One of the twenty most-watched Ready Set Do episodes on YouTube right now.
A lot of people frame success as leaving, which is convenient because it keeps the story simple. This episode is more interesting because he chose IIT Kharagpur instead, and the whole thing becomes a very different kind of bet on AI, ambition, and where the best launchpad actually is.
Who this is for
- You are trying to get hired without sounding like everybody else in the pile.
- You would rather hear Aayush's version while the mess is still fresh than get another polished hindsight sermon.
Key takeaways
- Survive the AI Wave as an Engineer (IIT Kharagpur Grad POV) - w/ Aayush
- "MS in US" Myth: Why staying in India might be the financially smarter move in the current economy.
- Tier-2 & Tier-3 Struggles: How to overcome the "subjective bias" of recruiters and break into top-tier tech roles.
- AI Roadmap: A step-by-step guide to preparing for a career in Machine Learning and AI, even if you are from a Civil or Ocean Engineering background.
- If you have ever wondered whether the obvious move is really the best move, this one has teeth.
- Naman introduces Aayush's decision as a challenge to the assumption that an ambitious Indian engineer must leave the...
- He argues that the value is not limited to one job offer. In industry, Aayush noticed that many directors, technical...
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Naman introduces Aayush's decision as a challenge to the assumption that an ambitious Indian engineer must leave the country for a useful master's degree. Aayush had taken the GRE and TOEFL and could have applied abroad, but chose an MTech at IIT Kharagpur. The episode asks whether that lower-cost route can create a serious launchpad for machine learning, placements, and long-term technical depth.
The conversation is organized around practical decisions: who an Indian technical master's degree is for, what the IIT Kharagpur AI experience is actually like, how an engineer should prepare for the AI wave, and why foundational AI work in India still trails the largest international labs. Naman asks Aayush to begin with the degree decision rather than the hype surrounding AI.
Aayush starts with the structure of Indian engineering education. He separates students from IITs and strong state institutions from those in tier-two and tier-three colleges, not as a judgment of ability but because campus recruiting and institutional access differ. In his experience, students from the latter group are more likely to view a master's degree as a second chance at the placement process.
For many students, the first motivation is placement. Aayush says young graduates often do not want to begin at a very low compensation level when they believe their early career should expose them to difficult work and faster learning. At colleges with weaker campus recruitment, getting a strong first role can be unusually hard, so admission to an IIT, IISc, or top NIT can reopen that door.
He argues that the value is not limited to one job offer. In industry, Aayush noticed that many directors, technical fellows, and deep subject-matter experts had advanced degrees. He does not claim that a master's is mandatory for leadership, but says rigorous graduate study can accelerate learning, expand the set of technical problems a person can approach, and create a stronger base for a long career.
Show notes
A lot of people frame success as leaving, which is convenient because it keeps the story simple. This episode is more interesting because he chose IIT Kharagpur instead, and the whole thing becomes a very different kind of bet on AI, ambition, and where the best launchpad actually is. If you have ever wondered whether the obvious move is really the best move, this one has teeth.
More in Get Hired
Same mess. Different guest. Pick the next conversation that feels closest to your real life.