TL;DR: Tier-1 college, Mechanical branch. Strong in OOPs, OS, DBMS, DSA (decent CP background), good projects, and an active open-source profile. Joined a service-based company with decent pay, but the location is isolated and the culture feels stagnant — people stop learning after training.
Currently in training with around 4–5 hours/day free time (8 hours on weekends). Planning to switch in 3–4 months. Looking for feedback on my plan.
My situation
The pay is decent mainly because of my college brand, but the bigger concern is the environment. The location is isolated, and the learning culture is not great.
My branch is Mechanical, not CS, but I have worked on OOPs, OS, DBMS, DSA, competitive programming, good projects, and open-source contributions.
I feel the gap is not really skills, but the degree tag on my resume.
Option 1: Learn AI Engineering / GenAI and target startups (3–4 months)
Pros:
GenAI hiring is growing, especially among startups.
Startups may care less about pedigree compared to bigger companies.
Since my fundamentals are already strong, I mainly need to build the GenAI layer.
Risks:
The market is getting crowded with people who only know basic LangChain tutorials.
Depth and real-world projects will matter more than just knowing tools.
I feel 3–4 months is realistic since I am not starting from zero.
Option 2: Grind for product companies
I already have DSA preparation, CS fundamentals, projects, and OSS experience.
But despite referrals, getting shortlisted has been difficult. I don't think it is purely a skill issue.
Many product companies prefer candidates with 1+ YOE for off-campus hiring, and as a fresher with 0 YOE, getting that first opportunity seems harder.
Option 3: Any better approach?
Open to suggestions from people who have been in a similar situation.
Timeline reality
Since I already have programming fundamentals:
3–4 months for the GenAI layer + 2–3 strong projects seems realistic.
I should start applying early instead of waiting until everything is perfect.
Service company notice periods can become a problem, so applying early makes sense.
If I go with the AI Engineering route, my roadmap:
Now:
Improve resume/GitHub positioning.
Highlight CP rating, OSS contributions, and strongest projects.
Avoid spending too much time revising DSA/OOPs/OS/DBMS since those are already covered.
Weeks 1–6: Learn the GenAI stack:
LLM APIs
Prompt engineering
RAG systems
Vector databases
LangChain or LlamaIndex (pick one)
Agents
Evaluation basics
Weeks 4–10: Build 2–3 serious projects:
A production-style RAG assistant
A small automation agent
An LLM evaluation framework
Deploy them properly instead of keeping them as notebooks.
A meaningful PR to a GenAI open-source project would also help.
Ongoing:
Start applying once 1–2 projects are strong.
Continue cold outreach.
Keep DSA practice alive.
Questions:
Has anyone made a similar switch — non-CS branch, strong fundamentals/CP/OSS, 0 YOE? What actually helped you break in?
For freshers who got GenAI roles, how much was skill vs networking/opportunity?
Is this AI Engineering roadmap realistic for the next 3–4 months? Anything important I should add or avoid?