Introduction

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Becoming AI Engineer

For the purposes of this site, we’re going to define an AI Engineer as an Applied AI Product Development role. Someone with more product development leaning experience and some machine learning experience or knowledge. As an AI Engineer, you’ll be expected to develop full-stack applications that utilize the latest AI tech and architectures to solve real customer problems. We’ll cover everything from training models to shipping AI products and iterating with customer feedback. We’ve built this website to help engineers level up their skills in AI. We hope that after completing some of these projects and following our advice you’ll be in a better position to get a job as an AI Engineer or start a company developing AI products. With these goals in mind, we recommend the follow high level steps to quickly get started.

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1. Orientation

1. Consume a large amount of content
2. Learn the vernacular
3. Watch tutorial videos and read blogs
4. Test out existing tools and products
5. Follow a bunch of twitter accounts and start interacting with the community
6. Setup your own social media presence

At this point, you should be familiar with a lot of the concepts and have some ideas of your own that you want to get started building. We highly recommend joining the twitter community and building in public. Follow a bunch of AI engineers, companies, and open source projects. Find the folks who are building and publishing their work.

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2. Tutorials and Projects

Choose a few of our more basic projects and tutorials to do on your own. The goal here is to take some of the theoretical knowledge your are starting to learn and apply it to something tangible. Take the time to set your local environment up nicely so you have a good playground to develop in. You should set some goals for completing a handful of projects by some date. Track your progress and post updates to twitter.

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3. Failure is the best teacher

Ideate a few projects or pick one of our more advanced projects to do on your own. Stretch yourselves and try to build something that you can show off. You’ll fail a bunch of times. That’s ok. That’s how you learn. Use Google and ChatGPT to figure out how to fix your mistakes. Record a daily demo of whatever progress or mess you’ve made. This will help you stay motivated and see the bigger picture. Post these to Twitter in a serial format. If you think you’ll be applying for AI Engineering jobs then pick a project similar to what you’d be building in that role. This will help you understand the problems that the role is solving and how you can contribute. If you can get into a great conversation during an interview about problems and various solutions that are relevant to the company then you’ve become a strong candidate.

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4. Projects to Product

Once you’ve got a few projects under your belt, start thinking about how to transform these demos into actual products. We’ve got a bunch of tutorials and projects that you can use to ship a real product. It is important to be able to take something running locally and ship it as a product. The last 20% can be the most difficult part of the process and being able to actually ship something is a huge strength of this field. Things to consider:

1. Customer Problem Solution
2. How much does it cost to run the product?
3. How much is the customer willing to pay?
4. How will you collect user feedback?
5. How will you improve the product based on user feedback?
6. Developer Experience

Start to think like a product manager. As an AI Engineer or a startup founder, you’ll be expected to understand how to identify problems, ideate, and ship products. Do not let decision paralysis prevent you from shipping something. You can validate assumptions you make by shipping something. Soon you’ll be able to leverage AI tools to do it faster than anyone else.

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5. Understanding

Your goal in this phase is to gain a deeper understanding of how to improve AI products. This can only be done with a deeper understanding of the underlying technology and how to incorporate feedback from users. We have a number of tutorials and projects designed to incorporate user feedback systems, write LLM evals, improve model performance through prompting and fine-tuning, and more.

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6. Mastery

This field is constantly being updated with new tools and technologies. Your goal is to not only keep yourself up to date on the latest and greatest but to also contribute your own expertise. Don’t just be a consumer; be a builder and creator.

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Starter Projects

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Choose Your Own Adventure

Checkout our skill tree to decide what path is right for you and the skills we think you’ll need as you progress in an AI engineering career. Here are some other useful jumping off points.