If I were starting out in AI Engineering now, I wouldn’t jump straight into AI Agents, RAG, or whatever LLM framework is popular on social media. Instead, I’d stick to a structured AI study plan, starting with Python and Machine Learning basics and working up to building real AI systems.
I’ve noticed this pattern while mentoring new AI Engineers. Most people don’t struggle because AI is too hard; they struggle because they try to learn everything out of order.
One week, they’re learning LangChain. The next, they’re fine-tuning an LLM. Then it hits them: they’re not comfortable with Python classes, APIs, vector similarity, or even basic Machine Learning ideas.
If I were starting over today, here’s the study plan I’d use.
AI Engineer Study Plan
Milestone 1: Build Strong Python Foundations
I’d start by focusing on Python. It might not be the most exciting part of AI Engineering, but almost everything you build later depends on how well you can write and understand Python code.
I’d begin with variables, data types, loops, conditions, functions, lists, dictionaries, and exception handling. Once I felt good about those basics, I’d move on to object-oriented programming, modules, virtual environments, and managing packages.
The tools I would learn at this stage are:
- Python
- VS Code
- Git and GitHub
- Jupyter Notebook
- pip and virtual environments
A common mistake I see beginners make is spending months on random Python exercises. You don’t need to be a competitive programmer before learning AI. Instead, I’d focus on building small, practical projects.
For example, I’d make a CSV data analyzer, an API data collector, and a simple command-line app. These projects help you learn how to organize code, work with files, handle errors, and use external libraries.
Milestone project: Build a Python application that collects data from an API, processes it, and generates a simple report.
Recommended Course: Python for Everybody Specialization.
Milestone 2: Learn the Mathematics Behind Machine Learning
I wouldn’t try to become a mathematician, but I’d make sure to learn enough math to really understand what Machine Learning algorithms are doing.
I’d focus on linear algebra topics like vectors, matrices, dot products, and matrix operations. After that, I’d learn about probability, distributions, mean, variance, covariance, and conditional probability.
For calculus, I’d mainly focus on derivatives, gradients, and gradient descent.
The goal isn’t to solve tough proofs. Instead, I’d aim to answer questions like:
- Why does gradient descent work?
- What does a loss function measure?
- Why do embeddings use vectors?
- How is cosine similarity calculated?
- Why does normalization affect model training?
In my experience, math gets much easier when you connect it to real code. I’d calculate cosine similarity with NumPy instead of just reading the formula. I’d also implement gradient descent for Linear Regression, not just memorize the update rule.
Milestone project: Implement Linear Regression and gradient descent using NumPy without Scikit-learn.
Recommended Resource: Mathematics for ML Book.
Milestone 3: Master Machine Learning Fundamentals
This is where I’d start learning how models actually learn from data. I’d begin with the full Machine Learning workflow:
Data collection → Data cleaning → Exploratory Data Analysis → Feature engineering → Model training → Evaluation → Deployment
Next, I’d learn supervised learning algorithms like Linear Regression, Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting.
After that, I’d study unsupervised learning topics like K-Means clustering, dimensionality reduction, and anomaly detection.
The tools I would master are:
Milestone projects:
- Customer churn prediction system
- Loan default prediction model and recovery
- Customer segmentation system
Recommended Resource: From ML Algorithms to GenAI and LLMs Book.
Milestone 4: Learn Deep Learning and Transformers
Once I felt confident with Machine Learning basics, I’d move on to Deep Learning.
I’d learn about neural networks, activation functions, loss functions, backpropagation, and optimizers. Then I’d get a basic understanding of CNNs and sequence models before diving deep into Transformers.
I’d spend extra time really understanding the Transformer architecture. I’d learn about:
- Tokenization
- Embeddings
- Positional encoding
- Self-attention
- Multi-head attention
- Feed-forward networks
I’d use PyTorch for Deep Learning and Hugging Face Transformers to work with pretrained models.
Milestone project: Build a text classification system using a pretrained Transformer model and Hugging Face.
Recommended Resource: From ML Algorithms to GenAI and LLMs Book.
Milestone 5: Enter Generative AI and LLM Engineering
At this point, I’d start working seriously with Large Language Models. First, I’d learn how LLM applications really work.
This means learning about prompts, system instructions, context windows, tokens, structured outputs, function calling, and model APIs.
Then I’d explore models from OpenAI, Google Gemini, Anthropic, and open-source options like Hugging Face.
The tools I would learn include:
I’d also learn prompt engineering, but I wouldn’t spend months on prompt templates. In real AI Engineering, prompts are just one part of a bigger system.
Milestone projects:
Recommended Resource: Hands-On GenAI, LLMs and AI Agents.
Milestone 6: Master RAG and Vector Databases
Retrieval-Augmented Generation is one of the most important things I’d learn as an AI Engineer. I’d start by understanding the whole RAG pipeline.
You collect documents, split them into chunks, turn them into embeddings, store them in a vector database, retrieve them with a query, and pass them to an LLM as context.
I’d study each part on its own. For embeddings, I’d look into Sentence Transformers and newer embedding models. For vector databases, I’d try out FAISS, Chroma, or Qdrant.
Then, I would learn advanced RAG concepts such as:
- Chunking strategies
- Metadata filtering
- Hybrid search
- Query rewriting
- Reranking
- RAG evaluation
One thing I’ve learned building RAG systems is that swapping out the LLM doesn’t always fix bad answers. Often, the real problem is poor retrieval.
Milestone project: Build an end-to-end RAG system that answers questions from multiple documents and includes source references.
Recommended Course: IBM RAG and Agentic AI Professional Certificate.
Milestone 7: Build AI Agents
I’d only start learning about AI Agents after I understood LLM applications and RAG.
An AI Agent isn’t just an LLM with a long prompt. I’d learn how agents use tools, keep track of state, make decisions, and run multi-step workflows.
The concepts I would focus on include:
- Tool calling
- Agent state
- Memory
- Planning
- Human-in-the-loop workflows
- Multi-agent systems
I’d check out frameworks like LangGraph and CrewAI, but I’d start by building a simple agent workflow on my own first.
Milestone projects:
Recommended Resource: Hands-On GenAI, LLMs and AI Agents.
Final Thoughts
If I were starting my AI Engineering journey today, I’d follow this learning plan. I wouldn’t rush through the milestones just to add more tools to my resume.
If I were starting again now, I’d learn things in the right order, build something at every milestone, and focus on understanding systems instead of just collecting tools.
I hope you found this article on the AI Engineer study plan helpful.
For more AI and machine learning tips, follow me on Instagram. My book, Hands-On GenAI, LLMs & AI Agents, can also help you advance your AI career.
