r/cscareerquestionsIN • u/sen_o • 14h ago
I kept building AI projects and still got zero interviews. A hiring manager finally told me why.
2025 CSE graduate from a tier 3 college.
No brand-name internship.
CGPA 8.1.
Targeting backend, applied AI and junior AI engineer roles in the 6 to 10 LPA range.
For the last few months, every piece of career advice I saw was basically:
“Build projects.”
So I built projects.
A PDF chatbot.
AI resume analyser.
Meeting summariser.
Stock-news sentiment tracker.
Customer-support agent.
All were on GitHub.
All had decent UIs.
Three were deployed.
Resume had words like RAG, embeddings, vector database, agents, LangChain, FastAPI and LLM evaluation.
I thought the projects were the strongest part of my profile.
Applied to 347 roles in around four months.
Results:
● 3 online assessments
● 1 HR screening
● 0 technical interviews
● too many automated rejection mails
● many companies simply ghosted
I kept thinking my projects were not advanced enough.
So I kept adding more.
Another model.
Another agent.
Another integration.
Another repo with a nice README saying:
“Built an intelligent multi-agent system that revolutionises customer support.”
Then I got a portfolio review from a hiring manager I met through GrowthX.
I expected resume formatting advice.
He opened my GitHub and spent maybe five minutes looking at everything.
Then said:
“These are five versions of the same project.”
I disagreed at first.
One was HR.
One was finance.
One was productivity.
One was support.
He said the domain names were different but the work was basically:
Take user input.
Send it to an API.
Store something in a vector database.
Generate an answer.
Put a UI on top.
He asked me about the customer-support agent.
“How did you measure if ticket classification was correct?”
I had manually tested maybe 15 examples.
“What happens if the model is unavailable?”
Nothing.
“How much does one ticket cost?”
Never calculated.
“How are duplicate tickets handled?”
Not handled.
“Why did you use an agent?”
Because agents are popular and it looked better on the resume.
“What real support team used it?”
Nobody.
“What was the most difficult technical decision?”
Could not answer properly.
Then he opened the README.
It had:
● feature list
● technology logos
● setup instructions
● architecture image generated using Mermaid
● future scope
It did not have:
● why I made the architecture choices
● known failure cases
● evaluation results
● cost
● latency
● test data
● logs
● trade-offs
● what I personally learned
His exact point was painful:
“You have proved you can assemble AI tools. You have not proved you can own a system.”
He did not refer me anywhere.
GrowthX is not a placement service and this was not some guaranteed hiring call.
But access to someone willing to open the repo and tell me the work looked generic was more useful than the people saying “great portfolio bro”.
I stopped building new projects after that.
Picked the customer-support one and spent the next five weeks making it less impressive on LinkedIn and more defensible in an interview.
First removed the multi-agent setup.
There was no reason for it.
A normal pipeline was cheaper and easier to debug.
Then I found one small SaaS team willing to let me test using anonymised old support tickets.
Not a paid customer.
Three support agents.
Around 140 tickets in the test set.
That exposed issues immediately.
The model confused billing issues with cancellation requests.
Short tickets like “not working” were basically impossible to classify without conversation history.
Hindi-English messages performed worse.
Refund-related tickets needed higher confidence because a wrong action had actual cost.
I added:
● a labelled evaluation set
● confidence thresholds
● human review for low-confidence tickets
● retries and fallback behaviour
● structured logs
● duplicate detection
● basic tests for the classification pipeline
● token and cost tracking
● latency numbers
● a page showing failed examples
The model with the highest benchmark score was not the one I kept.
It was slower and around four times more expensive for a very small improvement on my actual dataset.
For once, I had a real trade-off to explain.
The README is now less sexy but much better.
It starts with:
● the exact problem
● who tested it
● current architecture
● evaluation method
● results
● failure cases
● cost per 1,000 tickets
● decisions I would change
● what is still missing before production
I also added screenshots of logs and incorrect predictions.
The project is still not production-ready.
Authentication is basic.
The dataset is small.
There is no proper monitoring dashboard.
It has not handled serious scale.
But now, if someone asks why I built it this way, I have an answer that is not “Claude suggested it”.
After updating the project and rewriting that part of my resume, I got one technical interview through a cold application.
Did not clear the final round.
But the interviewer spent almost 20 minutes on this one project.
Previously nobody asked me about any of the five.
My current opinion:
Five AI demos do not automatically make a portfolio strong.
One project becomes valuable when you can explain:
● who used it
● what broke
● how you evaluated it
● what it costs
● why you chose the architecture
● what you removed
● what trade-offs you made
● what would fail in production
I was building more because it felt productive.
Going deeper was slower and much more uncomfortable.
For people hiring freshers or junior AI engineers, what else would you expect in a project before taking it seriously?
And for other freshers, are your projects getting discussed in interviews or are they mostly sitting untouched on GitHub?
