AI Engineer Jobs: How to Qualify Without a Traditional Degree

Breaking into AI Engineer Jobs: A Guide for Non-Degree Holders

If you’re looking to break into ai engineer jobs, you’re in the right place. I’ve been working in tech for over 15 years, and when I first started my career back in 2007, I really had no clue what I was getting into, but I’ve been fortunate enough to be part of lots of opportunities, like being an early engineer at a startup that was acquired for billions, and from then on, I became a software engineering manager, giving me valuable insights into what it takes to succeed in ai engineer jobs.

Now, I’m here sharing my insights and advice with you guys, specifically for those interested in landing those coveted ai engineer jobs or breaking into machine learning engineering.

If you’re interested in pursuing ai engineer jobs, this article is for you. I want to share a roadmap, a guide, or a blueprint – whatever you want to call it – for people who are getting into software engineering or getting started with software engineering, and more specifically, those interested in breaking into the world of ai engineer jobs or machine learning engineering. Whether you want to apply these steps to AI, machine learning, or any other field in software engineering, they will help you on your journey to landing those sought-after ai engineer jobs.

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Level 1: Start

At this stage, the main goal is to get moving. Many folks find themselves stuck at this level – it’s kind of like playing Monopoly but never leaving the start box. This is the stage where you break through your own barriers, get out of your own way, and start coding. You need to dive into building projects and start coding, even if the first few lines of your code turn out to be not so great. You’ll probably feel like banging your head against the keyboard at some point, but that is normal, and that is okay. It doesn’t mean you’re dumb, and it doesn’t mean that other people are smarter than you. It’s just part of the process.

At Level 1, you won’t know how to be a good AI engineer yet – that comes at Level 2 – but starting is the key here. There’s no way around this; you’ve just got to start coding. The Activation Energy Theory compares this to giving a little push to start a chemical reaction, and the Momentum Principle says it’s like a snowball effect, making it easier to keep going once you are in motion.

So, for all beginner AI engineers, career switchers, or CS students, my advice is to overcome the fear of a blank screen and start coding. Start with baby steps and build your first mini-project. It doesn’t have to be grand, and no one has to see it the first time.

If you’re not sure where to start or how to start, there are plenty of resources available online to help you find the perfect projects and get ideas on where to begin. The point here is to stop watching tutorials and start building. Things will start making more sense when you start being hands-on, and trust me, the more you do, the better you’ll be. Once you decide to get started on your first project, the next question is often: What do I make next? What language do I need to learn? At this point, it doesn’t really matter.

Just pick something that seems easiest for you and stop getting caught up in all the debates around language and tech stacks for beginners. Python is often recommended, especially for ai engineer jobs and machine learning, so unless you have a strong reason not to learn Python, just pick Python and start building a project around it. Your first project might be really simple and not very exciting, and that’s perfectly fine because the goal here at Level 1 is just to get started.

Choice #1: Give Up, Switch Tracks, or Keep Going

At the end of Level 1, you’re going to face an important choice to make. You have three options here:

Give up: If you’ve tried coding and maybe you feel like it’s a bit much for you or you don’t really feel passionate about it, it’s okay to acknowledge that engineering may just not be the thing for you. The important thing is that you gave it a shot, you tried it out, you were hands-on, and you built some things. Now you won’t have regrets thinking maybe you should have tried it when you were younger. You can say that you’ve tried it and move on.
Switch tracks: Maybe this particular thing you tried is not really your thing, and you want to explore a different type of software engineering, which is completely fine as well. There are so many types of software engineering, coding, and languages that you can choose from. You don’t have to stick with ai engineer jobs if that’s not your passion. If you choose this option, just go back to the starting point, Level 1, and get started with something else. You can pick web development, app development, or whatever you feel interested in – they’re all great options.
Keep going: Are you ready to commit to AI engineering and take it really seriously? If so, it’s time to move forward to Level 2. Becoming a really good AI engineer is not going to happen overnight, especially if you don’t have a Ph.D. or extensive years of experience working in the tech industry. You have a lot to catch up on here, but if you’re determined, you can do it.

Level 2: Level Up

Level 2 is all about becoming a better AI engineer. The goal here is to dive deep because becoming a really good AI engineer takes time and effort. At this level, you’re probably going to have to figure things out on your own, and it’s not going to be easy. Malcolm Gladwell talks about the 10,000-hour rule in his book “Outliers,” which emphasizes the importance of prolonged and focused practice to excel in any field.

If you can manage to land one of the coveted ai engineer jobs at this stage, good for you! But chances are, it’s going to be really hard to get a job because you’re not quite there yet. Most companies are going to want to hire you once you’re really good, so Level 2 is all about leveling up.

You’ll continue to build interesting projects but using different types of algorithms. Maybe you tried a few different things in Level 1; now it’s time to dig deeper into the same ones or add different types of algorithms to your portfolio. Your goal here is to build up your skill set, and each project you work on will contribute incrementally, pushing you forward by 1% over time. As you build more projects, you’re going to gradually move toward creating more quality programs and projects that other people care about. The path to leveling up may be different for every person, so keep trying different things to know what works for you. Keep iterating, keep improving.

You might be asking at one point, “What makes a good engineer?” I’m sorry to tell you there’s no consensus on this. Some people spend a good chunk of their lives chasing degrees and PhDs, and while that’s a fine route to pursue if that’s what you want to go for, it doesn’t have to be the only path.

If the idea of diving into a decade-long higher education program doesn’t really get you excited, that’s okay – but here’s the deal: You’ve got to make up for that time. Having a bunch of projects on your GitHub is not going to be enticing enough for employers to want to hire you, especially if those GitHub projects don’t even work in production. If you compare someone who’s been grinding away in a Ph.D. program for 10 years with you, who would you hire?

Again, it’s not about the credentials, degrees, or papers – it’s more about your skills. And there are many ways to level up. You can launch your own business, website, or app. You can volunteer for local charities or businesses to create something really interesting for them. You can take on contracting gigs, build projects for your clients, and get paid. There are many ways to gain hands-on, real-life experience.

Choice #2: Be Your Own Boss, Switch Tracks, or Keep Going

Now that you’ve leveled up your skills, you have some options to consider:

Be Your Own Boss: If you go with this option, you get to work on the projects that you find interesting. You may have the freedom to explore different areas of engineering, but there’s no guarantee that you’ll ever land a full-time job, and your income may be unpredictable. It also demands a high level of self-discipline and motivation to stay focused and productive on your own.
Switch Tracks: If you go with this option, there may be more opportunities in different areas of software engineering, potentially giving you a higher chance of landing your first role more quickly than in ai engineer jobs. Transitioning into a different role might provide a more immediate source of income, and you could learn new things that you might find even more interesting. However, depending on the role, it might mean you have to go back to Level 1 and learn a few more skills, and it might even take you further away from AI engineering. You might end up hating it anyway.
Keep Going: You can keep trying to become an AI engineer and keep looking for those elusive ai engineer jobs. The competition is going to be really tough, and there aren’t a lot of entry-level positions in AI engineering, but if you’re really passionate about becoming an AI engineer, this could be the right path for you. It is definitely going to be an uphill battle, but if you’re determined, you can probably do it.

As you think about these options, consider your career goals and aspirations, as well as your skill set and the current job market demands. But no matter which route you go, the skills you learn in Level 1 and Level 2 will still help you become more successful in the long term in your software engineering or AI career.

Level 3: Strategize

Level 3 is where we’re going to talk more specifically about your job search strategy and how to increase your chances of landing that perfect dream job in ai engineer jobs. Let’s take a look at where you are in your job search.

Step 1: Fix Your Resume

If you’ve started job searching and you’re not getting any calls from recruiters, there might be a problem with your resume. Here’s a little stat for you: If you apply to about 100 jobs, you should be hearing back from recruiters about 10 to 20 times. That’s just the average statistic, and if you’re hearing back less than that, it means something is off with your resume.

If you’re in that situation, I encourage you to check out resume resources online or talk to your school’s career counseling services (which are often included in your tuition). If you’re at a boot camp, try talking to the boot camp staff for guidance. Studies show that recruiters only spend about 6 to 8 seconds looking at each resume, mainly to spot potential red flags because they don’t have a lot of time to comb through every single resume due to the high volume they receive. So, make sure your resume is clear, concise, and highlights your relevant skills and experience for ai engineer jobs.

Step 2: Practice Your Interview Skills

Let’s say you did fix all the red flags on your resume, and now you’re getting those interviews, but you’re not getting the job offers. This means you need to practice your interview skills. How do you talk during your interviews? Can you do the coding challenges? You just need to practice answering these common questions, as they tend to ask the same things over and over.

Do mock interviews and try to get more feedback to improve your interview skills. Again, there are programs you can enroll in to get help with interview prep, but if you can’t afford one of those, do it with your friends. A lot of people are studying for interviews, so you can do it together and find some buddies for accountability.

If you’re going solo, you can also try recording yourself on your phone – it doesn’t have to be anything fancy. Just make a video of yourself doing a mock interview or a coding challenge. See if you make any funny faces, listen to your voice, and observe how you sound. Do you sound confident? You can probably learn a lot by watching yourself in the video.

There are also plenty of free interview prep resources available online, so be sure to take advantage of those as you prepare for your ai engineer jobs interviews.

Step 3: Learn Negotiation Skills

If you’re getting job offers but not the type of ones you like, maybe you want to learn negotiation skills. Research industry standards and use websites like levels.fyi to help you know what your value should be. There are also resources available online about which software engineering jobs pay the most, so you can check those out to see what the different levels are supposed to pay.

But if you’ve made it to this stage, I’m pretty sure you’re going to be okay. So, big congratulations!

Conclusion

Breaking into ai engineer jobs without a degree is possible, but it requires dedication, hard work, and a strategic approach. By following this three-level roadmap – Start, Level Up, and Strategize – you can increase your chances of landing your dream job in AI engineering, even if you don’t have a traditional degree in the field. Remember, when it comes to securing ai engineer jobs, it’s not just about the credentials or degrees; it’s about your skills, your passion for the field, and your ability to demonstrate your value to potential employers.

So, keep learning, keep building, and keep pushing forward, and with persistence and determination, you can achieve your goals and build a successful career in ai engineer jobs, even without a traditional degree.

FAQ

What does an AI engineer do?

AI engineer jobs involve designing, developing, and deploying artificial intelligence systems and applications. They work on creating intelligent algorithms, machine learning models, and neural networks that can learn from data and make decisions or predictions, which is a core aspect of ai engineer jobs. Those in AI engineer jobs often collaborate with data scientists, software developers, and other professionals to create innovative AI solutions for various industries, such as healthcare, finance, marketing, and transportation.

What are the job requirements for AI engineers?

The job requirements for AI engineers may vary depending on the company and the specific role. However, most AI engineer positions typically require:

A bachelor’s or master’s degree in computer science, mathematics, statistics, or a related field
Strong programming skills in languages such as Python, Java, C++, or R
Experience with machine learning frameworks and libraries like TensorFlow, Keras, PyTorch, or scikit-learn
Knowledge of data structures, algorithms, and software development principles
Familiarity with cloud computing platforms like AWS, Google Cloud, or Azure
Excellent problem-solving, analytical, and communication skills
Ability to work collaboratively in a team environment

What skills are required for AI engineers?

To become a successful AI engineer, one should possess a combination of technical and soft skills. Some of the essential skills include:

Proficiency in programming languages like Python, Java, C++, or R
Strong understanding of mathematics, statistics, and probability concepts
Knowledge of machine learning algorithms and techniques, such as supervised learning, unsupervised learning, deep learning, and reinforcement learning
Familiarity with natural language processing (NLP), computer vision, and speech recognition techniques
Experience with data preprocessing, feature engineering, and model evaluation
Ability to work with large datasets and data structures
Understanding of software development methodologies and best practices
Strong analytical, problem-solving, and critical thinking skills
Excellent communication and collaboration abilities

What is the salary of an AI engineer?

The salary for ai engineer jobs can vary depending on factors such as location, experience, company size, and industry. According to Glassdoor, as of April 2023, the average base pay for professionals in ai engineer jobs in the United States is approximately $119,300 per year. However, salaries for ai engineer jobs can range from around $90,000 to over $200,000 per year, depending on the aforementioned factors.

It’s important to note that AI engineering is a highly competitive and rapidly evolving field, and salaries for ai engineer jobs may change based on market demand and the specific skills required for the role.