is the new “hot” role in the tech scene, and many people are desperate to land this job.
I see so many posts online saying how you can become an AI engineer in a few months.
Let me be clear: anyone telling you that you can become an AI engineer in six months is selling you a dream.
The reality is that it will take longer, but that doesn’t mean you can’t try to fast-track the process.
If you’re new here, I’m Egor. I work as a machine learning engineer and am also a career coach for people breaking into data, AI, and machine learning.
I’ve seen firsthand what works and what’s just a waste of time.
Let’s get into it!
What Is An AI Engineer?
Let’s clarify exactly what an AI engineer is, as there is a lot of confusion online.
I have a separate article explaining the key differences, but in a nutshell an AI engineer is a software engineer who specialises in the use and integration of foundational GenAI models such as Claude, GPT, BERT, and others.
They don’t “build” these models from scratch like a data scientist or machine learning engineer; rather, they use them to serve a specific purpose.
For example, they may embed a chatbot on a shopping website to help customers find what they are looking for more quickly, or add a coding assistant in an IDE, like Cursor.
As AI engineers are specialised software engineers, they need to know the fundamental practises of software engineering and have a strong knowledge of AI systems.
This skillset is rare but in high demand nowadays due to the hype around AI. So naturally, the salary of AI engineers is very high and many companies pay around $200k–$300k, according to levels.fyi.
As you can see, it’s a pretty attractive career with a lot of growth potential. Let’s now go over exactly how you can become one.
Step 1: Entry Point
One unfortunate reality is that it is extremely difficult to break into AI engineering with zero previous experience.
This is because the profession requires sufficient expertise across data, machine learning, software engineering, and, naturally, AI.
Therefore, you will need to become a data scientist or a software engineer for at least one year before thinking of pivoting to becoming an AI engineer.
Whether you become a data scientist or a software engineer is up to you and your background.
However, I personally recommend starting as a software engineer first, as it’s more closely related to the AI engineering role.
You also don’t have to take my word for it; Greg Brockman (OpenAI CTO) also agrees that it’s better to be a software engineer first and then improve your AI/ML knowledge.
As a software engineer, you should make an effort to learn the tools and technologies that are needed to become an AI engineer, these include:
- Python — The whole AI/ML ecosystem is built in Python, so you should be able to write solid production code in this language.
- SQL — AI revolves around data, and SQL is the language of Data.
- Software Development Tools — Need to know things like git for version control, zsh/bash basics and understanding how to create and use APIs.
- System Design Technologies — The AI system you will eventually build will need to scale, and you will likely deploy it on cloud platforms like AWS, Azure, GCP using tools like Docker and Kubernetes.
Resources
Timeline
The timeline depends on how long it takes you to land a software engineering or data science job.
Being practical, if you have a STEM background with some solid knowledge, and you really apply yourself, you can land jobs in these roles in about 6 months.
You should then stay in this role for about a year before trying to make the switch to AI engineering to ensure you have covered your basics.
There are many guides online on how to break into software engineering, and I have several roadmaps for becoming a data scientist that you can also check out.
Step 2: AI/ML Fundamentals
Alongside your full-time work as a software engineer, you will need to up-skill yourself on the basics of AI/ML to ensure you are making quick progress in your journey.
You certainly don’t need to have a PhD in Maths level of understanding, as you won’t build these models from scratch, but it will give you background details to dive deeper into more advanced topics at a later date.
These are the things you should know:
- Maths Fundamentals — A solid overview of statistics, probability, linear algebra and calculus will help you understand what’s happening under the hood.
- Supervised Learning — Know how the basic algorithms like linear regression, decision trees and support vector machines work.
- Unsupervised Learning — Know how the basic algorithms like K-Means and K-Nearest-Neighbour work.
- Neural Networks — These are the backbone of LLMs, and having a good understanding of topics like backpropagation, vanishing gradients and activation functions will allow you to debug AI models quicker in the future.
- Basics of LLMs — Even though you won’t be building LLMs from scratch, you will be working with them every day, so it’s good to have some knowledge about how they operate. You should learn about areas such as transformers, autoencoders, tokenisation, and embeddings.
Resources
Timeline
Learning the fundamentals will depend on exactly how long you study while working as a data scientist/software engineer.
The recommendation is to integrate these concepts into your daily work as much as possible.
If I were studying all of this outside of working hours, I would anticipate it would take 3–6 months if you apply yourself.
Step 3: AI Engineering Concepts
At this point, it’s time to dive deeper into the specific concepts and ideas you will be using as an AI engineer in the real world.
This field is evolving rapidly, and every month there is a new “thing” to learn. I will list the timeless fundamentals here as they are by far the most important.
- AI APIs — Services like OpenAI’s API let you integrate powerful models without needing to build them yourself. This is the fastest way to start building real applications with AI capabilities.
- Prompt Engineering — Learning how to effectively communicate with AI models is a crucial skill. Well-crafted prompts can dramatically improve model outputs and are essential for getting consistent results.
- Retrieval Augmented Generation (RAG) — Understand how to connect to LLMs to external databases like Pinecone and use related information to improve the accuracy of the AI model’s responses.
- Model Context Protocol (MCP) — The standardised way to connect your AI models to external applications like files, servers and other apps.
- LangChain — This is the best package for working with AI models in Python. It provides all the architecture you need to build and connect LLMs seamlessly.
- Fine-Tuning — Understand how to improve the performance of an AI model by training it on specific data so it is better at responding and giving outputs for a certain use case.
Resources
Timeline
Learning these concepts will take slightly less time than learning AI/ML fundamentals, as there is less material to cover.
I would anticipate it would take about 2–3 months to learn everything to a good standard.
Step 4: Projects
There is a lot of confusion of what projects you should build in order to get a job in AI engineering
To put it simply, the best projects are ones that are intrinsically motivating for you and also benefit some sort of end user or client.
Here are the high-level steps:
- Idea — Brainstorm ideas and topics that are personal to you and a problem you want to solve. This should come from your own thoughts and research; don’t look online or ask people like me for project ideas. Anything I give you will immediately be a bad project for you.
- Data — Find novel and exciting data using public APIs, government websites, web-scraping, etc. You want to replicate the messy data you will encounter in the real world.
- Deploy — You need to showcase your ability to deploy AI systems end-to-end. This will include data storage, data cleaning, model connection then some integration on the front end through an API or even a web app. You need to match the work you will be doing as a full-time AI engineer as closely as possible.
- Document — No one will know about your project if you don’t tell people about it. Do a LinkedIn post, write a blog article and add it to your portfolio. Make sure your project has a clear, well-organised README on GitHub so people can test it for themselves. Share your work as much as possible, as it will increase your chances of being seen by potential employers.
Timeline
Creating good projects and building a solid portfolio will take time. Ideally, you should build two top-tier projects should take you about 3 months in total. This assumes you can dedicate 1 hour per day to building these.
Step 5: Getting Hired
This could be a whole post in itself, but let me give you the high level 80/20 of what you should do:
Resume
To write a great resume, make sure everything is specifically about AI engineering:
- Have your technical skills right at the top with relevant tools and technologies for AI engineering roles.
- Make your projects clearly visible with metrics, figures, and, particularly, the financial impact.
- Keep it simple: neutral colours, single column, easy-to-read fonts, and only a page long.
- List your relevant experience as either a software engineer or a data scientist.
I have a full article on how to make a great resume that you can check out below, as well as a ready-made template you can use.
LinkedIn
Make your LinkedIn profile obvious that you are going for AI engineering roles:
- Your headline should contain “AI Engineer”, no “aspiring” please. For example, who would want to hire an “aspiring” dentist?
- Include keywords throughout your “About me” and “Experience” sections, but add them organically and don’t write paragraphs.
- Make your profile aesthetic with a clear photo and a nice-looking banner. This makes a bigger difference than you think.
Referrals & Networking
Most people think they need to build loads of projects and take endless courses to stand out and get a job.
That is a complete waste of time.
Referrals are the golden ticket for any tech job.
According to a study, referrals account for 7% of applications but 40% of all hires. If you’re referred, you’re almost 6x more likely to get your dream job.
That leverage is crazy.
The way you get a referral is actually fairly simple, and all it requires is some confidence on your part.
- Find companies hiring for AI engineers or companies you’d like to work for.
- Browse their employees on LinkedIn and find someone similar to you. This could be someone with the same university and background, ideally an AI engineer as well.
- Connect and send them a DM containing something you liked about their profile, journey or anything personal. Never ask for a referral in the first message.
- Chat to them and ask them questions about their work, projects and anything cool they are doing.
- After a few messages, that’s when you ask for a referral or any feedback on your resume.
The process is so simple, the problem is people are just too scared to do it.
However, I have never had a bad experience, because you always lead with a compliment or an opener about them.
People love talking about themselves, and all you need to do is come across as friendly and show that you are interested in them.
Timeline
Getting a job can vary a lot, and it can also come down to luck sometimes. However, by actually going after referrals and avoiding distractions from projects and courses, this should take 6 months.
So, to become an AI engineer, it will take you, optimistically, about 2 years, but you also need to land a job as a software engineer or data scientist first.
This may seem like a long time, but these roles are highly skilled and pay ridiculous salaries. You can’t expect to do a couple of courses and walk straight into them.
If after reading this article, you really want to become an AI engineer, that is great!
However, like I just mentioned, you need to become a data scientist first. Fortunately, in one of my previous articles, I wrote exactly the steps I would follow if I were to become a data scientist again.
I will see you there!
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