I'm a software engineer with zero AI knowledge. Here's my brutally honest 180-day plan to become job-ready in AI development.
The Turning Point
Three months ago, I realized something: I'm a solid software engineer, but I have a massive blind spot. I don't understand AI.
Not really. I can use ChatGPT, I know what transformers are in theory, and I've heard of LLMs. But if someone asked me to build an AI system from scratch? I'd be lost.
So I did what any engineer does: I decided to solve the problem myself.
Instead of taking another 6-month online course (that I'll probably abandon), or going back to school (not happening), I'm committing to 180 days of intensive, structured AI learning.
And I'm doing it publicly.
Why Public Learning?
This is the unusual part. I could learn privately, quietly, then announce "I'm now an AI expert!" in 6 months.
But I'm not going to.
Here's why:
1. It forces accountability. If I post my progress publicly, I can't ghost this project. Strangers are watching. 😅
2. It builds reputation. By documenting my journey, I become visible. Potential employers, clients, and collaborators will see my learning process, my mistakes, my breakthroughs.
3. It helps others. Someone out there is probably in the exact same situation: solid engineer, zero AI knowledge, intimidated by the learning curve. My real journey—including failures—is worth more than a polished "how to" article written by an expert.
4. It generates income. (Let's be honest.) Writing about my learning on Medium/Dev.to could generate if I do it right. That's real money from sharing knowledge.
My Background (Why This Matters)
I'm a backend/full-stack engineer with 4+ years of professional experience. I can:
- Build scalable systems
- Write clean, testable code
- Debug production issues under pressure
- Understand system architecture
- Learn new technologies quickly
But I've been deliberately avoiding AI because:
- The math seemed too hard
- I didn't know where to start
- Most tutorials felt either too basic or too theoretical
- I thought I needed a PhD
All of that was bullshit. The tools and resources exist. I just needed to commit.
The 180-Day Plan (Overview)
I've created a detailed 180-day curriculum combining:
- The original "180-Day AI Engineer" plan (structured)
- Fast.ai (practical, top-down learning)
- Google's ML Crash Course (fundamentals)
- Hugging Face courses (modern LLMs)
- My own project-based learning (real applications)
The 9 Phases:
| Phase | Days | Goal |
|---|---|---|
| Phase 1 | 1-25 | Python + ML Foundations |
| Phase 2 | 26-50 | Neural Networks + Deep Learning |
| Phase 3 | 51-75 | LLMs + Transformers |
| Phase 4 | 76-100 | LLM Application Engineering |
| Phase 5 | 101-125 | RAG + Vector Databases |
| Phase 6 | 126-150 | Fine-tuning + Quantization |
| Phase 7 | 151-175 | Agents + Agentic AI |
| Phase 8 | 176-190 | Agent Frameworks + MCP |
| Phase 9 | 191-210 | Production AI + Business |
Special focus: Agent/Agentic AI (the most interesting and lucrative area right now)
What I'm NOT Doing
- I'm not getting a degree
- I'm not spending $10K on bootcamps
- I'm not watching 100-hour YouTube playlists
- I'm not pretending this will be easy
- I'm not learning theory without building projects
What I AM doing:
- Building real projects (not tutorials)
- Learning from the best free resources
- Publishing my learnings weekly
- Shipping code to GitHub
- Monetizing along the way
The Monetization Angle (Real Talk)
By day 210, I want to be:
- Employable as an AI engineer
- Building freelance AI projects
- Running small AI SaaS products
- Consulting on AI architecture
This is aggressive, but not unrealistic if I execute.
What I'm Sharing (And Why You Should Follow)
Every 1-2 weeks, I'll publish:
🧠Technical Deep Dives:
- "Building a Neural Network from Scratch (No Framework)"
- "How Attention Actually Works (Visual Explanation)"
- "RAG vs Fine-Tuning: When to Use Each"
- "I Built an AI Agent That Debugs Code"
📚 Real Learnings:
- Mistakes I made and how I fixed them
- Concepts that finally clicked
- Resources that actually worked vs. the hype
🤖 Projects I'm Building:
- Links to GitHub repos
- Live demos
- Open source contributions
The Challenge
Here's my commitment:
✅ 180 days of learning (minimum 2-4 hours/day)
✅ 1-2 blog posts per week (technical + personal)
✅ Complete all major projects (not skipping anything)
✅ Share code on GitHub (working, imperfect code > polished excuses)
✅ Be brutally honest (including failures)
✅ Build to $1K+/month in AI-related income by day 180
If I miss a week, I'll explain why. If a project fails, I'll explain the failure. If I discover better resources, I'll course-correct publicly.
Why You Should Care
If you're an engineer (like me):
- You'll see a realistic path to AI without leaving software engineering
- You'll learn from someone who thinks like you (pragmatic, systems-focused, impatient)
- You'll get actual code and projects, not just theory
If you're considering AI:
- You'll see if it's worth your time
- You'll get a real learning plan you can follow
- You'll know what's hype vs. what's real
If you're already in AI:
- You might learn a perspective from someone new to the field
- You can critique my approach and help me improve
- You'll see what genuinely works for self-teaching
Starting Today (Day 1)
I started this week.
Day 1 was Python basics. Sounds boring? It was. But it's day 1 of 180. I'm not trying to be impressive. I'm trying to be consistent.
Tomorrow: Functions and data structures.
By day 10: My first AI project.
By day 30: First income-generating deliverable.
By day 180: Professional AI engineer.
How to Follow Along
I'll be publishing:
- Medium: In-depth technical posts + income reports (best for algorithmic reach + money)
- Dev.to: Quick learnings + project updates (best for community)
- GitHub: All code, projects, resources (source of truth)
Follow me to:
- Get real-time learning updates
- See unfiltered progress (wins and failures)
- Join the conversation
- Maybe even follow your own 180-day journey
Final Thought
There's this myth that becoming an AI engineer is either:
- A) Something only PhD mathematicians can do
- B) Something you can do in a weekend with the "right bootcamp"
Both are wrong.
It's hard but doable. It takes structure, consistency, and good resources. It's not magic—it's engineering.
I'm going to prove it. Publicly. For 180 days.
Want to watch? Or better yet—want to do it with me?
Comment below. Tell me:
- Are you learning AI too?
- What's holding you back?
- What would help you most?
Let's do this together.
Day 1 complete. 179 days to go.
About Me
I'm a full-stack engineer with 4+ years of experience. I've built scalable systems, led teams, and shipped products. But I've never built AI systems—until now.
- LinkedIn: [linkedin.com/in/surya-anand-04a12b1b7]
I'll be updating my progress every week. Hit follow so you don't miss it.
Next week: I'll dive into why I chose this specific learning plan over the alternatives. Plus: The top AI resources that actually work (and why most fail).
Top comments (1)
Great, you are doing well,
but i found some lacks, there is no mention of RAMP framework, without learning RAMP how can you become a Ai Engineer