Your 6-Month AI Career Roadmap

Honest situation assessment

You're a student with zero work experience, solid web dev skills (React, Node, Flask), and self-rated 1–2 across the ML stack. That's exactly where most people start. Here's what nobody tells you: your web dev background is a secret weapon. Most ML students can't build a frontend. You can ship things end-to-end. That matters enormously.

Your newsletter bot proves you can execute — not just learn. That's more than 80% of people asking for plans like this. Now we need to upgrade it from a Layer 3 system (LLM executes actions) to a proper 3-layer architecture: predict → decide → act. That's the ₹80K difference.

Your biggest risk is not knowledge gaps. It's "analysis paralysis" disguised as "I need to learn more first." You don't. You need to build more.

Target role

AI/GenAI Engineer

Target income

₹80K / month

Timeline

24 weeks

Weekly hours

10–15 hrs

Focus areas

RAG + Agents

Geography

India / Remote

🔤 Your new LinkedIn headline (replace it today)

Building RAG + Agentic AI Systems | AIML Student | Shipping AI that actually works end-to-end

System 1 → System 2 spectrum (your current position)

System 1 only (Layer 3)₹80K zone ↑Full System 2

You're at ~2/10. ₹80K jobs sit at 6–7/10. The green marker is your target. Your newsletter bot is Layer 3 (LLM executes). This plan gets you all three layers.

Skill value audit — your resume, run through the formula

Python LOW value

Table stakes. Literally everyone lists this. It's like a chef putting "knows how to use a knife" on their resume. Keep it, but never lead with it. It's a tool, not a differentiator.

Java, C++ LOW value (for AI roles)

These are interviewing languages, not AI job-getters. Keep them on your resume but stop investing time here. Zero scarcity in the AI context.

React + Node.js + Flask MEDIUM-HIGH — your edge

Underrated. Most ML engineers can't build a frontend demo. You can ship full-stack AI apps. A GenAI engineer who also builds the interface? That's rare and valuable. Double down on this — it's your competitive advantage as a student.

HTML, CSS, JavaScript LOW — fold into full-stack

Don't list these separately. Fold them into "Full-Stack AI Application Development." Listing HTML on a resume in 2025 is noise, not signal.

ADD: RAG System Design (end-to-end) HIGH — primary skill to build

Every company wants internal knowledge bases, document Q&A, customer support bots. Revenue: massive. Scarcity: high (most people call APIs, almost nobody designs proper RAG). This is your #1 skill to develop.

ADD: Agentic AI with Verification Loops HIGH — what separates ₹5L from ₹20L+

The market is flooded with "I used LangChain" engineers. Almost nobody builds agents with proper decision + verification layers. This is the skill that turns your ₹80K target from a stretch goal into table stakes.

6-month milestones

Month 1

LinkedIn headline overhauled. 4 posts published. Transformer paper read. Dead-simple RAG system built locally. Newsletter bot upgraded with Layer 2 decision logic. 500+ connection requests sent. Outreach started.

Month 2

RAG system functional with evaluation metrics. Verification layer added to newsletter bot. 8 LinkedIn posts published. 1000+ connections sent, 50+ DMs sent. 8 papers read. First real conversations with people at target companies.

Month 3

Project 1 (RAG system) deployed publicly. RAPTOR-style retrieval implemented. 12 LinkedIn posts. 1500+ outreach connections. 12 papers read. 3–5 real calls with target companies.

Month 4

Project 2 (agentic system, all 3 layers) in progress. Strong opinions forming on production RAG. 16 LinkedIn posts. 2000+ outreach. Starting to get inbound interest from content.

Month 5

Both projects deployed and documented on GitHub. Mock interviews happening. 20+ posts. 2500+ outreach. 5–10 real opportunities in pipeline (calls, freelance inquiries, interview processes).

Month 6

2 production projects live on GitHub. 24+ LinkedIn posts published. 3000+ outreach messages sent. 12+ papers internalized. Offer(s) in hand or strong pipeline. ₹80K target within reach — likely exceeded.

      ☁ Progress auto-saves locally and syncs to cloud on every change. Use the sync bar at the bottom to set up cloud sync.
    

Post 3–4x per week. These are actual drafts — not templates.

Edit 2–3 lines to make each sound like you, then publish. Tap any card to expand and copy. Start with Post #1 today.

The outreach machine

You said "yes absolutely" to cold outreach. Good. Daily targets: 25 LinkedIn connection requests + 5 personalized DMs. Monthly volume: ~800 connections, ~150 DMs. Volume is the game — simple as that.

Where to find targets: AI startups in India that raised seed/Series A in last 18 months (Crunchbase), US AI startups open to India remote (look for "remote" in job postings, DM founders not HR), CTOs at Indian SaaS companies building internal AI tools, AI consultants in India with 500+ followers (they subcontract).

12-week reading list — 1 paper per week

For each paper: read abstract + introduction first. If you understand it, go deeper. If not, find a YouTube explanation first, then come back. The limitations section of every paper is a free roadmap of what gets built next — read it carefully.

All free resources

Local only