It’s no secret that AI’s foothold on society grows larger and larger. According to a Deloitte survey, 60% of professionals already use it in their day-to-day tasks, and almost 50% fear losing their jobs to AI.
Automation and fear of replacement by AI are nothing new. What if we turned the problem on its head and started thinking about using AI to better our career prospects?
Enter the position of AI engineer, for which experts already estimate there are a whopping 27 million job openings.
As with every nascent career path, many trials, stepping stones, and obstacles exist. If you’ve already started learning about AI, read on to learn what steps you need to take to turn your knowledge into a lucrative career.
What is an AI engineer?
An AI engineer specializes in developing and implementing algorithms and models that enable machines to perform tasks that typically require human intelligence. Think of it as creating an artificial brain with all its capabilities but none of its ephemeral nature and fragility.
Unlike traditional electric or computer engineering, an AI engineer must master many different fields, with taking a course in data science or earning a degree being all but mandatory. However, the exact combination of these fields depends on where you intend to work as an engineer.
Someone, for instance, looking to work at an enterprise level will have to have an in-depth knowledge of business analytics, while an AI engineer at a software dev startup can get by just knowing how to use OpenAI’s APIs.
Furthermore, their responsibilities include designing and refining AI models, data preprocessing, selecting appropriate datasets for training models, tuning parameters, and integrating AI functionalities into applications or products. They also ensure the ethical use and implementation of AI technologies, focusing on transparency, fairness, and privacy. Hence, it’s no wonder why AI engineers are the modern version of the old Rennaissance Man.
How much can an AI engineer make?
With new career paths, it’s often hard to gauge the situation on the market, which is why it’s pretty challenging to give an exact ballpark. According to Glassdoor, the annual median number is currently at a not-so-shabby $151,000, but as we said, this depends on many factors.
Someone close to sitting on the OpenAI board will undoubtedly be making six or even seven figures, while startups might only look for AI engineers on a contractual basis. The point is—conduct research properly, contact people already in the industry, and ask the right questions. Most people will be more than willing to help an aspiring AI engineer out.
How do I get started as an AI engineer?
Although many would tell you that the barrier to entry is high, that isn’t exactly true. Yes, most AI engineers have master’s degrees, PhDs, and even research experience, but that’s only because AI engineering is a relatively new field.
Before, the only way to learn more about deep learning advanced JavaScript libraries for AI and everything else was through demanding postgraduate courses. But times have changed, and a PhD is no longer mandatory.
That’s why today, a great and engaging way to begin your journey is self-paced training through ML and AI bootcamps and courses that allow you to learn fundamental concepts in your own home at your own speed.
Another avenue is to seek instructor-led learning through virtual courses or at an academic institution. Last but not least, many leading US colleges already have top-tier AI programs, but for this one, you have to think—am I ready to commit 4+ years, countless hours, and taking on a six-figure sum in student debt?
What are some of the fields that future AI engineers should study?
The pillars of AI are computer science, math, and IT. Future AI engineers should have a strong base by learning the programming languages that lay the bedrock for advancement. These include Java, JavaScript, and Python, but don’t be spooked by the occasional appearance of C and C#.
Remember that learning several languages is a mammoth commitment in and of itself, so the best way to accelerate your progress is to jump head-first into a coding bootcamp.
Okay, so you’ve learned the basics of coding and still want to continue? If so, statistics, linear algebra, and deep learning are next. Only then can you move on to AI and ML algorithms.
Forming a framework and leveraging Big Data analytics
AI engineers must be experienced with frameworks—building blocks to architect, train, validate, and deploy models through high-level programming interfaces. The best examples are TensorFlow and PyTorch, derived from JavaScript and Python.
Furthermore, AI and ML collect, analyze, and extrapolate massive data sets. Managing these datasets requires considerable data training, knowledge of analytics engines like Apache Spark, and MongoDB for scale-out architecture, and the open-source Apache Hadoop to facilitate network-based problem-solving.
How to land a job as an AI engineer
Once you’ve got the know-how, it’s time to begin a job hunt. As mentioned, many employers will require at least a bachelor’s degree, though it’s not always necessary. More and more people are finding success with various certifications, such as software engineering certifications, earned from professional programs.
Regardless of your path, navigating the job market as an AI engineer requires a blend of technical prowess, strategic job searching, and networking finesse. Here’s how you can enhance your job hunt:
- Optimize your LinkedIn profile. Your LinkedIn should be a beacon for recruiters. Ensure your headline explicitly states you’re an AI Engineer. Populate the summary section with your specialties in AI, projects, and any impactful outcomes you’ve achieved.
- Tailor your resume for each application. Generic resumes don’t cut it. Customize your resume to highlight experience and projects relevant to the job description. Use ATS-friendly keywords from the job listing, but do not overuse them. Even resumes can be spammy.
- Don’t limit yourself to fixed roles. Likewise, you can also become a freelancer of sorts. Let’s say you develop a document generation system powered by OpenAI’s API and sell it as a SaaS product. However, this route is only recommended to those with previous contracting or freelancing experience.
- Showcase your projects: Whether on GitHub, a personal website, or LinkedIn, make your projects accessible. Detail the problem, your solution, the technologies used, and the outcome. Projects demonstrate your practical skills beyond what can be shown in a resume.
- Apply to companies of all sizes: Again, without a PhD and research experience, don’t expect to work in a lab or on government-funded projects. Alternatively, if you feel more comfortable working on practical projects, such as Workday staff augmentation with AI features, there will be plenty of slots for engineers at companies, especially large enterprises.
- Contribute to open source: Contributing to open-source projects can significantly boost your visibility and credibility. It shows your commitment to the AI field, your ability to collaborate on complex projects, and your coding skills.
- Network strategically: Attend AI-specific conferences, workshops, and meetups. Engage with the AI community on platforms like GitHub, Reddit (subreddits like r/MachineLearning), and specialized AI forums. Networking isn’t just about meeting potential employers; it’s about learning from peers, discovering opportunities, and staying on top of industry trends.
Since you’re here…
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