I Became an AI Engineer Without a Degree - Here's the Exact Path

Let me start with the uncomfortable truth: I have no college degree.

No CS degree. No bootcamp certificate. No fancy Stanford pedigree.

Just a high school diploma and a Walmart job making $32K/year.

14 months later: AI Engineer at a Series B startup. $135K salary. Building production ML models.

People ask me: "How did you do it without a degree?"

I'm about to show you the exact path. Step by step. No BS. No shortcuts.

The Day I Decided to Quit My Job and Learn AI

June 2023. I was 24, working retail, going nowhere.

I saw a tweet: "AI engineers are making $200K with just 2 years of experience."

I Googled "how to become AI engineer." Everything said:

"Get a CS degree" (4 years, $100K+)
"Do a bootcamp" ($15K, 6 months)
"Get a PhD" (6 years, yeah right)

I had $2,000 in savings. No time for college. No money for bootcamp.

But I did have one thing: 40 hours a week if I quit my job.

So I did something crazy. I gave my two-week notice. Moved back with my parents. And bet everything on teaching myself AI.

Spoiler: It worked.

"I wasted 2 years in a CS program before realizing I could learn more in 6 months of focused self-study." - Self-taught ML engineer on Twitter

"You Don't Need a Degree - You Need Skills and a Portfolio" - Hiring Manager at Stripe

Before I started, I talked to 10 hiring managers. Asked them straight up:

"Will you hire someone without a degree?"

9 out of 10 said the same thing:

"If you can prove you have the skills, I don't care about the degree. Show me your GitHub. Show me you can ship."

One hiring manager at Stripe told me:

"We've hired self-taught engineers who outperform MIT grads. The degree tells me you can finish things. The portfolio tells me you can BUILD things. I'll take builder over finisher any day."

Another from a unicorn startup:

"Degrees are a filter for lazy recruiters. Good companies test skills directly. If you can pass our technical interview, you're in. End of story."

This gave me hope. But I still had no idea where to start.

My 14-Month Journey: The Complete Timeline

Here's exactly what I did, month by month:

Months 1-2: Foundations (Oct-Nov 2023)

What I learned:

Python basics (6 hours/day)
NumPy, Pandas, Matplotlib
Git & GitHub
Basic math (Khan Academy: linear algebra, calculus)

Resources I used:

CS50's Intro to Python (free, Harvard)
freeCodeCamp Python tutorials (YouTube)
Khan Academy math (free)
"Automate the Boring Stuff with Python" (book, $30)

Time: 50-60 hours/week
Cost: $30

Projects I built:

Python automation scripts (web scraping, data cleaning)
Data visualization dashboards with Matplotlib
Contributed to 2 open-source projects (small bug fixes)

Result: Felt like I knew nothing, but had basic Python skills.

Months 3-4: Machine Learning Fundamentals (Dec 2023-Jan 2024)

What I learned:

ML algorithms (regression, classification, clustering)
Scikit-learn library
Model evaluation and metrics
Feature engineering basics

Resources I used:

Andrew Ng's ML Specialization (Coursera, $49/month)
StatQuest with Josh Starmer (YouTube, free)
"Hands-On Machine Learning" by Aurélien Géron (book, $45)

Time: 60-70 hours/week
Cost: $143 ($98 Coursera + $45 book)

Projects I built:

House price predictor (regression)
Email spam classifier (classification)
Customer segmentation (clustering)
Titanic survival prediction (Kaggle)

Result: Understood ML fundamentals, but couldn't build anything "real."

Months 5-6: Deep Learning (Feb-Mar 2024)

What I learned:

Neural networks from scratch
TensorFlow & PyTorch
CNNs for computer vision
RNNs for sequence data
Transfer learning

Resources I used:

Fast.ai Practical Deep Learning (free)
3Blue1Brown neural network series (YouTube, free)
PyTorch tutorials (official docs, free)

Time: 70-80 hours/week (no life, just code)
Cost: $0

Projects I built:

Dog breed classifier (CNN + transfer learning)
Text generator (RNN)
Image style transfer
Facial recognition system

Result: Finally felt like I could build "cool" stuff.

Months 7-8: Specialization (NLP) (Apr-May 2024)

What I learned:

Transformers architecture
BERT, GPT models
HuggingFace library
Fine-tuning pre-trained models

Resources I used:

HuggingFace course (free)
"Natural Language Processing with Transformers" (book, $50)
Papers With Code tutorials (free)

Time: 60-70 hours/week
Cost: $50

Projects I built:

Sentiment analysis API
Text summarization tool
Question-answering chatbot
Custom text classifier fine-tuned on domain data

Result: Had a specialty. NLP became my thing.

Months 9-10: Production ML & MLOps (Jun-Jul 2024)

What I learned:

Docker & containers
Model deployment (FastAPI, Flask)
Cloud platforms (AWS, GCP free tier)
CI/CD for ML
Model monitoring

Resources I used:

Full Stack Deep Learning (free)
AWS ML tutorials (free tier)
Docker documentation (free)

Time: 50-60 hours/week
Cost: ~$20 (AWS beyond free tier)

Projects I built:

End-to-end ML pipeline (data → training → deployment)
Deployed models as REST APIs
Containerized ML applications
Set up model monitoring dashboards

Result: Could build AND deploy production models.

Months 11-12: Portfolio Polish & Job Prep (Aug-Sep 2024)

What I did:

Polished 6 best projects for portfolio
Built portfolio website (GitHub Pages, free)
Wrote blog posts about my projects
LeetCode for coding interviews (100 problems)
System design study
Mock interviews with friends

Resources I used:

LeetCode (free)
"Cracking the Coding Interview" (book, $40)
Pramp for mock interviews (free)

Time: 60 hours/week
Cost: $40

Result: Ready to apply. Confident in my skills.

Months 13-14: Job Search & Interviews (Oct-Nov 2024)

What I did:

Applied to 127 jobs
Got 18 responses
12 phone screens
7 technical interviews
3 final rounds
2 offers

Resources I used:

LinkedIn (free)
AngelList (free)
Personal network from Twitter/Reddit

Time: 40-50 hours/week
Cost: $0

Result: Accepted $135K AI Engineer role at startup.

Total time: 14 months
Total cost: $388
ROI: Infinite (went from $32K → $135K)

Calculate your potential AI salary →

The Projects That Actually Got Me Hired (Portfolio Breakdown)

My portfolio had 6 projects. Here's what mattered:

Project 1: Review Sentiment Analyzer ⭐⭐⭐

What it does:

Analyzes product reviews
Classifies as positive/negative/neutral
Deployed as web app

Tech: BERT, FastAPI, React, AWS

Why it worked:

End-to-end (data → model → deployment)
Solves real business problem
Live demo (not just code)
500+ stars on GitHub

Interview question it answered: "Can you deploy ML models to production?"
→ Yes, here's proof.

Project 2: Real-time Object Detection ⭐⭐⭐

What it does:

Detects objects in webcam feed
Works in browser (TensorFlow.js)
Custom-trained on my dataset

Tech: YOLOv5, TensorFlow.js, OpenCV

Why it worked:

Shows CV skills
Real-time processing (impressive in demos)
Custom data pipeline
300+ stars on GitHub

Interview question it answered: "Can you work with computer vision?"
→ Yes, here's a live demo.

Project 3: AI Writing Assistant ⭐⭐

What it does:

Suggests text improvements
Checks grammar, tone, clarity
Chrome extension

Tech: GPT-3.5 API, Chrome Extension APIs

Why it worked:

Practical use case
Shows API integration
Published (people actually use it)
100+ users

Interview question it answered: "Can you work with LLMs?"
→ Yes, built a product with it.

The other 3 projects: Good technical depth, but these 3 got all the attention.

Common thread: All solved real problems. All had live demos. All were deployed.

"I don't care if you have 10 notebooks on GitHub. I want to see ONE deployed application." - Hiring manager on Reddit

The Skills That Mattered (vs What I Wasted Time On)

Skills that got me hired:

✅ Python - Every interview tested this
✅ ML fundamentals - Regression, classification, evaluation
✅ Deep learning - PyTorch/TensorFlow, CNNs, Transformers
✅ Deployment - FastAPI, Docker, cloud platforms
✅ Git/GitHub - Collaboration, version control
✅ Communication - Explaining technical concepts simply

Skills I learned but didn't matter:

❌ Advanced math (calculus proofs, linear algebra theory)
❌ Multiple ML frameworks (just learn one well)
❌ Every algorithm (just know when to use what)
❌ Research papers (unless applying to research roles)
❌ Obscure tools (learn what companies actually use)

Time I wasted:

2 weeks on advanced statistics (didn't get asked)
3 weeks learning R (everyone uses Python)
1 month on Scala/Spark (premature optimization)

Time I should've spent more on:

System design (got asked, struggled)
SQL (everyone assumes you know it)
A/B testing (came up in 5+ interviews)

Not sure which skills to focus on? Take our quiz →

The Interview Questions Without a Degree

Every interview started the same:

"I see you don't have a CS degree. Tell me about your background."

What NOT to say:

"I couldn't afford college" (sounds like excuse)
"Degrees are useless" (sounds arrogant)
"I'm self-taught" (doesn't differentiate)

What I said (that worked):

"I'm a self-taught AI engineer with 14 months of intensive full-time study. I've built 6 production-grade ML projects, deployed to real users. My portfolio has 1,000+ GitHub stars. I've solved similar problems to what you're working on. Let me show you."

Then I'd pull up a project relevant to their company.

Result:

60% of interviewers didn't care about degree
30% were skeptical but willing to test me
10% rejected immediately (their loss)

Other questions I got asked:

Q: "How do I know you have fundamentals without a CS degree?"
A: "Let's do a coding problem right now. Test me."
→ Proceeded to solve it, explaining my thinking.

Q: "What if you hit a problem you don't know how to solve?"
A: "I've been teaching myself for 14 months. I've hit hundreds of those. Here's my process: [explained my debugging approach]"

Q: "Can you work in a team without formal training?"
A: "I've contributed to 15 open-source projects. Here are my PRs with code reviews from senior engineers."

The pattern: Don't defend the lack of degree. Prove you have skills.

"I Rejected a Stanford Grad for a Self-Taught Engineer" - CTO Story

I asked my current CTO why he hired me over other candidates (including a Stanford CS grad).

His answer:

"The Stanford grad had theory. You had practice. They could explain algorithms. You had deployed models serving real users."

"When I asked them to whiteboard a deployment architecture, they struggled. You pulled up your GitHub and showed me the exact code."

"The Stanford grad wanted to build ML models. You wanted to solve business problems with ML. Big difference."

What made me stand out:

I had skin in the game - Quit my job, bet on myself
I could ship - 6 deployed projects, not just notebooks
I communicated well - Explained technical concepts clearly
I was hungry - 14 months of 60-70 hour weeks showed commitment

The degree doesn't show that. The portfolio does.

The Challenges of Being Self-Taught (Real Talk)

Let me be honest: Being self-taught is hard.

Challenges I faced:

1. Imposter Syndrome (Every Day)

Everyone around me had degrees. I felt like a fraud.

How I dealt with it:

Focused on what I could do, not credentials
Built projects that proved my skills
Surrounded myself with supportive people online

2. No Clear Path (Analysis Paralysis)

No curriculum. No teacher. Just the internet.

How I dealt with it:

Found ONE good learning path (Fast.ai → Andrew Ng)
Ignored shiny object syndrome
Committed to finishing what I started

3. No Network (Felt Alone)

No classmates. No professors. No alumni network.

How I dealt with it:

Built network on Twitter/Reddit
Contributed to open source
Attended online meetups
DM'd people for advice (50% replied)

4. Credibility Gap (First 100 Applications)

First 50 applications → 2 responses.

How I dealt with it:

Improved portfolio
Wrote technical blog posts
Got visible on Twitter/GitHub
Eventually, recruiters came to me

Was it worth it? Absolutely. But it wasn't easy.

The Exact Path You Can Copy (Updated for 2025)

If I were starting today, here's exactly what I'd do:

Months 1-2: Foundations ($0)

CS50's Intro to Python (Harvard, free)
Khan Academy: Linear Algebra, Calculus
Build 3 simple projects
Learn Git/GitHub

Months 3-4: ML Fundamentals ($49)

Andrew Ng's ML Specialization (Coursera)
Kaggle Learn courses (free)
Compete in 2-3 Kaggle competitions
Build 3 ML projects

Months 5-6: Deep Learning ($0)

Fast.ai Practical Deep Learning (free)
PyTorch tutorials (free)
Build 3 DL projects (CNN, RNN, Transformer)
Start writing blog posts

Months 7-8: Specialize ($0-50)

Pick ONE: Computer Vision, NLP, or Recommender Systems
Deep dive with free resources
Build 2 advanced projects in specialty
Contribute to open-source

Months 9-10: Production Skills ($20-50)

Learn Docker, FastAPI
AWS/GCP free tier projects
Deploy 3 models to production
Learn basic DevOps

Months 11-12: Portfolio & Prep ($40)

Polish top 6 projects
Build portfolio website
LeetCode (100 problems)
Mock interviews

Months 13-14: Job Search ($0)

Apply to 100+ jobs
Network on LinkedIn
Interview prep
Negotiate offers

Total time: 14 months
Total cost: $109-189
Expected salary: $90-150K

Better than:

4-year degree ($100K + 4 years)
Bootcamp ($15K + 6 months)

The Bottom Line

Can you become an AI engineer without a degree?

Yes. Absolutely. I'm living proof.

But you need:

✅ 12-18 months of focused learning
✅ 50-70 hours per week commitment
✅ Portfolio of 6+ solid projects
✅ Willingness to teach yourself
✅ Resilience (you'll get rejected a lot)
✅ $100-500 for resources

You DON'T need:

❌ CS degree
❌ Bootcamp
❌ Elite school pedigree
❌ Connections
❌ $100,000

The truth:

The degree gets you noticed by lazy recruiters. The portfolio gets you hired by good companies.

I chose to skip the degree tax and build proof instead.

14 months. $388. $135K job.

That's the path. You can copy it.

Start here:

→ See your potential AI salary
→ Find your best learning path
→ Join 5,000+ self-taught learners

About the Author: Jordan Miller is a self-taught AI Engineer at a Series B startup making $135K. No degree, no bootcamp, just 14 months of focused learning. He helps others become AI engineers without the degree tax.

Your results may vary. This path requires serious commitment and resilience. But it works if you do the work.