AI Engineer
6-Month Roadmap to Become Job-Ready (Based on Shub’s Viral Tweet)
If you had 6 months to go from zero to job-ready AI Engineer, what would you focus on? Tech creator @shub0414 laid out this exact 12-stage roadmap in a widely shared tweet — and it’s one of the clearest, most actionable paths available.
This is the traditional, technical AI Engineer route: heavy on coding, math, models, and production skills. Perfect if you love deep technical problem-solving and want to build the AI systems themselves.
The 6-Month AI Engineer Roadmap
| Stage | What You Learn | Why It Matters |
|---|---|---|
| 1 | Python Basics Syntax, loops, functions, OOP, NumPy, Pandas | Foundation for everything in AI/ML development |
| 2 | Math for AI Linear algebra, statistics, probability, basic calculus | You need this to actually understand how models work under the hood |
| 3 | Machine Learning Regression, classification, clustering, metrics (scikit-learn) | Core traditional ML skills that power most real-world applications |
| 4 | Deep Learning Basics Neural networks, CNNs, RNNs, training fundamentals (PyTorch/TensorFlow) | Modern neural network fundamentals that power today’s AI breakthroughs |
| 5 | Modern AI / LLMs Prompt engineering, embeddings, RAG, fine-tuning small models | The current hot area — large language models and generative AI |
| 6 | Build AI Projects Chatbots, classifiers, NLP apps, image models | Hands-on practice — this is where theory turns into real skills |
| 7 | GenAI Tools LangChain, Hugging Face, vector databases (FAISS, Pinecone) | How to actually ship production-ready LLM applications |
| 8 | MLOps Essentials FastAPI/Flask, Docker, GitHub, cloud deployment basics | Productionizing models — what separates hobbyists from professional engineers |
| 9 | Full End-to-End Projects Complete ML pipelines and deployed AI apps | Portfolio builders that show you can deliver real value |
| 10 | Portfolio 5–7 polished projects with READMEs + demo videos | What recruiters and hiring managers actually look at |
| 11 | Job Prep LeetCode basics, system design, ML/AI interview questions | Getting ready to land the offer |
| 12 | Apply AI Engineer, ML Engineer, Data/AI roles, GenAI developer positions | Turn all that hard work into a job |
Who This Path Is For
This roadmap is coding-heavy and math-intensive — exactly what most “AI Engineer” job postings expect. It’s the classic technical path for coders, math lovers, and builders who enjoy going deep into algorithms, models, and infrastructure.
Time to proficiency: 6+ months of intensive, consistent work (plus ongoing learning as models evolve).
See How It Compares
AI Operator
The faster, lighter-touch alternative: orchestrate agents, write agents.md files, redesign business workflows. No heavy math or model training required.
Explore AI Operator Path →AI Engineer vs AI Operator
Side-by-side breakdown of the two roles, skills, timeframes, and who each is best suited for.
Full Comparison →Original roadmap tweet by @shub0414