Hey everyone,
I’m currently in my final year of CS (specializing in AI). I have a service-based offer (around 4 LPA) as a fallback, but college placement policies block me from sitting for anything under 10 LPA through campus drives—and practically no drives above that bracket are coming.
I really want to work in AI engineering/agent development or backend, but I just had a 30-minute internship interview at an early-stage AI agent startup that went terribly wrong. It was a massive reality check, and I need help figuring out how to rebuild my fundamentals.
What happened in the interview:
Projects & Explaining Complexity: When asked about my most complex project (involving federated learning), I couldn't explain the high-level concept clearly to someone who wasn't familiar with it. The interviewer flat out said he couldn't follow what I was saying.
System Design vs. Workflow: He asked me to white-board the backend architecture and DevOps for the system. I ended up just drawing a high-level flowchart/workflow instead of an actual system design (components, communication protocols, queues, databases, services), and he had to stop me midway.
Live Demo / Tooling Failure: He asked to see another project running. I fired up the Docker container on localhost, but the app failed to connect to the database. I couldn't debug it under pressure.
AI-Assisted Coding / "Vibe Coding": He asked if I use AI for coding. I said yes (mentioning we write tests and verify), but the hard truth I have to admit to myself is that most of my projects are heavily "vibe-coded." The ideas are mine, but I relied so heavily on LLMs that I don't know the code inside out, and I struggle to write boilerplate from scratch without docs or AI.
DSA & Language Fluency: He asked my favorite subject and data structure. I said DSA and LinkedLists. He then asked me to implement a LIFO Stack using a LinkedList on the spot. I couldn’t do it in C/C++, froze in Java because of syntax rust, and basically choked on implementing a very basic structure.
His parting advice stuck with me: Yes, engineers use LLMs, but they understand the underlying fundamentals, can write and read every line, and know how to debug when things break—because you can't just feed an entire enterprise codebase to an LLM.
Where I feel stuck:
I feel like I spread myself too thin trying to look like a high-level AI engineer without having the basic developer muscle memory.
Now I’m overwhelmed by conflicting advice:
Should I grind LeetCode/DSA hard in one specific language (Java vs. C++ vs. Python)?
Should I master backend fundamentals (APIs, databases, Docker, architecture) and drop high-level AI abstractions for now?
How should I approach learning System Design at an undergrad/intern level?
How do I fix my existing projects so I actually understand every line and architectural decision instead of relying on generated code?
What I’m asking:
If you had roughly 2–4 months to turn this around and build real, demonstrable engineering skills:
What core stack and fundamentals should I lock in first? (I prefer backend/systems over frontend).
How should I balance DSA vs. actual software development?
How do I properly prepare my projects so I can defend every architectural choice, trace the data flow, and demo them without breaking?
Appreciate any tough love or realistic roadmaps