How AI is changing your work
Many companies are hiring fewer freshers for routine coding work that AI now assists with, and more for people who can build with AI from day one.
GenAI application roles often don’t require years of experience. They need demonstrable skill in LLMs, RAG, agents and deployment.
Freshers with cloud deployment skills and a public portfolio stand out from the thousands of applicants who only list courses.
What you already bring to AI
College programming basics
A head start in the Python phase, if you paid attention in class.
Maths from your degree
Helps with ML concepts, but you don’t need to be a maths topper.
Time to practise
Your biggest advantage over working professionals.
Mini and final-year projects
Can be upgraded into AI projects for your portfolio.
Your 6-month roadmap
Same live classes as every other learner, with the examples, projects and interview preparation angled toward where you're coming from.
- Months 1–2
Industry-grade Python, Git, Linux and FastAPI, the fundamentals college often skips.
- Month 3
Machine learning, deep learning and NLP with hands-on labs.
- Months 3–4
Generative AI, RAG and vector databases. Start your portfolio.
- Months 4–5
Agentic AI with LangGraph and MCP: the project that carries interviews.
- Months 5–6
AWS deployment, resume and LinkedIn, GitHub polish and weekly mock interviews.
Roles you can target
Junior AI / GenAI engineer
Build LLM applications and RAG systems.
Python developer (AI team)
Backend and API work for AI products.
Associate ML / MLOps engineer
Deployment and pipelines for AI systems.
The honest challenges
A career switch into AI is very doable, and it isn't easy. Here is what to expect:
- Placement is not guaranteed. Your projects and interview performance decide outcomes.
- Balancing exams and the course takes discipline, so plan around exam months.
- Copying code from tutorials won’t help in interviews. You must understand every line of your projects.
Projects in your portfolio
- Production RAG chatbot over private company documents
- Multi-agent workflow built with LangGraph
- Fine-tuned domain-specific LLM
- NLP service serving live predictions through FastAPI
- Full MLOps deployment on AWS with CI/CD and monitoring