TL;DR: AI engineering is THE fastest growing tech career right now – everyone’s talking about it, and for good reason. With great salaries and high-demand across industries, it’s an appealing career. You’ll want to develop practical skills like Python, basic machine learning, and APIs. Don’t forget to create AI-powered projects for your portfolio. Whether you’re a beginner or career changer, AI engineering is a doable path if you stay consistent and keep learning as the space evolves.

LinkedIn’s annual Jobs on the Rise ranked “AI engineer” as the fastest-growing job title in the US.

Even though it’s a popular field, you may think the role of an AI engineer is unreachable. You might think it’s reserved for someone who has a tech degree and breathes calculus as if it were oxygen. Here’s the secret: it’s within reach for you too.

Like any other tech role, you can achieve it by starting to learn the basic skills and gradually upleveling.

In this guide, you’ll get an introduction to an AI engineer’s day-to-day life. Let’s look at their salaries, different experience levels, and, most importantly, the practical roadmap to becoming an AI engineer.

Table of Contents

• What Does an AI Engineer Do?
• Everyday Examples of AI Engineering
• What is The Average Salary for AI Engineers?
• What is The Expected Growth for AI Engineer Jobs?
• How to Become an AI Engineer With No Experience?
• Is AI Engineering Right for You?
• FAQs

Is Tech Right For you? Take Our 3-Minute Quiz!

You Will Learn:

☑️ If a career in tech is right for you

☑️ What tech careers fit your strengths

☑️ What skills you need to reach your goals

What Does an AI Engineer Do?

AI engineering transforms machine learning models and large language models (LLMs) into real, usable products. It bridges the gap between what AI can do and what the product needs it to do.

You might confuse an AI engineer with a researcher. But a researcher focuses on theoretical advancements. An AI engineer is someone who tackles real business challenges by using existing tools and pre-trained models.

An AI engineer’s work involves:

• Building and Integrating AI Systems: Connecting AI models to apps, websites, and workflows via APIs and data pipelines.
• Data Work: Cleaning, organizing, and managing the data that AI systems use to train.
• Testing and Monitoring: Checking if the AI systems are behaving as expected and fixing them if they don’t work.
• Deployment: Picking AI models from the development environment and taking them into production.

An AI engineer’s day-to-day tasks might look like this:

• Investigating why a model’s recovery rate suddenly dropped.
• Debugging issues in an LLM-powered assistant.
• Comparing advanced retrieval techniques with fine-tuning.

However, the scope of the job depends on where you work. In startups, an AI engineer handles nearly everything. Whereas in larger tech companies, the roles are more specialized.

Everyday Examples of AI Engineering

It’s almost a guarantee that you have daily interactions with AI engineering projects. Here are a few things you might’ve used this week that an AI engineer helped build:

• Streaming Recommendations: When scrolling through Netflix and Spotify, you encounter some suggestions and end up loving them. That’s possible because an AI engineer built a recommendation model.
• Spam Filters: Your inbox only contains important emails and not junk. AI systems are responsible for constantly sorting and flagging emails for authenticity.
• Customer Support Chatbots: Sometimes the fastest way to get customer support is to use the chatbot on the company’s website. An AI engineer integrated a language model and programmed it to give you helpful information.
• Voice Assistants: Did you ask Siri to set an alarm? That’s speech recognition and natural language processing (NLP), two core domains of AI engineering.
• Health Wearables: Rings and watches track sleep, stress, and vital signs to give you health insights. This intelligence was also built by AI engineers.
• Autocorrect and Predictive Text: When you’re writing an email, you may notice that it does tasks like autocorrecting misspellings. Machine learning (ML) and NLP work to keep your writing error-free.

AI is not a side dish or an add-on. It’s embedded into systems. It runs everything from fraud checks to supply planning. Behind every system, there’s an AI engineer who helped bring it to life.

What is The Average Salary for AI Engineers?

According to Glassdoor, the median salary for AI engineers is $142,000. Overall, an AI engineer’s salary can range between $114,000 to $180,000.

Many factors can determine how much money you make as an AI engineer. However, it’s clear that even entry-level AI engineers are well paid. As your skill stack and experience grow, your salary will too.

What is The Expected Growth for AI Engineer Jobs?

AI engin4eering is not a trend; it’s a shift. Almost every industry is trying to figure out how to integrate AI into its systems.

The US Bureau of Labor Statistics predicts that roles in computer and information research will grow 20% between 2024 and 2034. While the future projections are promising, the demand for AI engineers is already here. One study found that 7 out of 10 fastest-growing roles in tech are AI-related.

Although AI is worth knowing, specialization matters. The engineers who go deep into specific areas have the most opportunities and the highest salaries.

Is Tech Right For you? Take Our 3-Minute Quiz!

You Will Learn:

☑️ If a career in tech is right for you

☑️ What tech careers fit your strengths

☑️ What skills you need to reach your goals

How to Become an AI Engineer With No Experience?

You don’t need a tech degree or years of experience to become an AI engineer. All you need is a dedication to learn the right skills and some proof that you can do work.

Learn the Right Skills

Focus on building strong coding skills, such as:

• Python: A non-negotiable is learning Python since it’s the main coding language used for AI.
• ML Basics: Understanding how ML models train from data is a foundational skill.
• LLM Fundamentals: Core skills include prompt engineering, embeddings, and working with AI APIs.
• Data and Cloud Essentials: Learn SQL, data cleaning, and entry-level cloud knowledge.

Choose a Learning Path

A tech degree is a traditional route, but it’s not the only one to become an AI engineer. You could also choose to sign up for bootcamps and online courses. Platforms like Codecademy, Coursera, and Google’s free AI courses are great paths for career changers. This path can take 6 to 12 months if you’re disciplined and consistent.

Build a Portfolio

Your portfolio is your advocate when you don’t have considerable experience in AI yet. Start with building small AI projects like a chatbot using an LLM API. Other AI projects include a recommendation system or an image classifier. Put your projects on GitHub and write proper READMEs.

Prep for Technical Interviews

Technical interviews in AI are different from your regular interviews. You’ll have to explain your problem-solving approach. You may also need to complete coding challenges. Practicing coding and mock interviews can help you prepare for this step. Once you do all of these with sheer dedication, you can easily crack any technical interview.

Apply for Jobs

Here are a few key steps to take before applying for tech jobs:

• Update your LinkedIn.
• Tailor a resume, adding key projects and skills.
• Make a dynamic portfolio.
• Start networking.

Is AI Engineering Right for You?

If debugging code gives you a dopamine hit, it’s a good sign that you would like AI engineering. Other promising signs are if you enjoy problem-solving, building usable products, and learning new skills.

If this sounds like your path, browse current AI engineering roles to understand what companies are looking for right now.

FAQs

Do I Need a Degree to Become an AI Engineer?

You don’t need a degree to become an AI engineer. Companies focus on practical skills, projects, and problem-solving ability, rather than the level of education. A strong portfolio that demonstrates your skills can help you get a job as an AI engineer.

How are AI Engineers Different from Data Scientists and ML Engineers?

AI engineers sit closer to the software; their duty is to pick AI capabilities and put them to work. Meanwhile, data scientists focus on analysis and insights. ML engineers build and train models. Think of it as data scientists analyze possibilities, ML engineers build over them, and AI engineers navigate the ship.

Is Tech Right For you? Take Our 3-Minute Quiz!

You Will Learn:

☑️ If a career in tech is right for you

☑️ What tech careers fit your strengths

☑️ What skills you need to reach your goals