How to Get a Job in AI

The global AI market reached \$196 billion in 2023 and is projected to grow at a compound annual rate of 37% over the next decade.

This explosive growth has made AI a top priority for companies. McKinsey reports that over a third of companies now use AI in their business functions, while a Conference Board survey shows that 56% of U.S. workers are already using generative AI tools at work.

AI is revolutionizing everything—pushing the boundaries of what's possible in science, medicine, transportation, and beyond.

The silver lining to this AI boom? The job market is booming right along with it. AI is expected to create 97 million jobs by 2030. Plus, jobs utilizing AI skills command up to a 25% wage premium compared to other roles.

Want to get in on this growth? This article provides a comprehensive roadmap on how to get a job in AI, guiding you through every step from building your knowledge base to securing your ideal position.

Here's what we'll cover:

Table of Contents

Understanding AI Job Roles

AI Salaries

Educational Background and Requirements for AI Jobs

Essential Skills and Tools for AI Careers

Building a Strong AI Portfolio

Experience and Internships

The Job Application Process

Transitioning into AI

How to Get a Job in AI with 365 Data Science

FAQs

Understanding AI Job Roles

AI job roles encompass a diverse range of positions, all focused on harnessing data and artificial intelligence to tackle complex problems. The various roles—from data scientist to AI engineer—are distinguished by their specific tools and methodologies.

AI specialist roles have become some of the most coveted positions in the tech industry. LinkedIn, for example, has identified AI engineer as one of the fastest-growing jobs in the United States in 2024.

These specialized positions typically require a unique blend of technical expertise and domain knowledge.

If you’re wondering how to get a job in AI, here’s an outline of some of the most important roles this year:

AI Strategist

The AI strategist aligns AI initiatives with a company's broader business strategy.

They identify use cases where AI can enhance products, streamline processes, and improve decision-making.

Combining technical expertise in AI and machine learning with strategic business insight, AI strategists guide the implementation process and advocate for AI adoption throughout the organization.

AI Developer

AI developers build foundational AI models, a process that involves in-depth research, neural network architecture design, and meticulous data preparation.

This role requires advanced technical skills, including expertise in Python, statistics, and deep learning.

Due to its complexity and the need for specialized knowledge, the role of an AI developer is among the most demanding in the tech industry. But the AI developer salary is also among the most competitive, as we will see below.

AI Engineer

AI engineers integrate existing AI foundation models into various products, such as websites, mobile applications, and IoT devices.

They bridge the gap between these models and real-world applications, optimizing their performance through methods like fine-tuning.

AI engineers need to be proficient in coding, particularly in Python, and familiar with tools like Hugging Face and Langchain​.

While the skills required for these roles are complex, the financial rewards can be substantial—a key motivator for exploring jobs in AI.

AI Salaries

According to Glassdoor, AI engineer salaries in the US in 2024 are impressive. Top earners can command over \$265,000 annually, while those in the lowest percentiles still earn around \$157,000. Keep in mind that this is total annual pay, including bonuses.

These figures vary based on factors such as experience, company, and location. For instance, the data from Ziprecruiter (in the table below) indicates a different, but still competitive, average annual AI salary. It also demonstrates that states like California, Washington, Texas, and New York are among the top-paying locations for AI professionals.

Here’s a look at the average annual salaries for other common careers in AI, according to Glassdoor:

As we've seen, artificial intelligence careers can be highly lucrative, especially when it comes to AI strategist or AI engineer salaries. So, how can you secure a job in the AI field?

Educational Background and Requirements

If you're wondering how to get a job in AI, you likely fall into one of these two categories:

• A student exploring educational paths to secure your dream position
• An established professional in another field (tech or non-tech) looking to transition into AI

Your starting point—whether you're a student or a professional—will shape your learning and AI career path.

Whichever category you find yourself in, however, getting a job in AI requires a combination of a university degree, certifications, and advanced AI skills.

Let's examine these prerequisites and discuss what's really essential for launching a career in AI.

Degrees and Education

Most AI-based roles require a bachelor’s degree in computer science, data science, statistics, mathematics, or a related field. Many advanced positions, however, require a master’s or PhD.

Over 60% of job openings for ML engineers that we studied require a degree in computer science. The other top qualifications, such as engineering, machine learning, data science, statistics, and mathematics, follow closely behind.

But even without the "right" degree, you can still break into the AI field if you're determined and motivated.

AI roles prioritize practical skills and experience over formal credentials. While a technical background is beneficial, many companies now value demonstrated expertise through online courses, bootcamps, and personal projects.

Building an AI portfolio can significantly enhance your job prospects.

If you don’t know where to start, 365 Data Science offers ready-made projects to help you gain experience and boost your portfolio.

Certifications

Certifications are valuable in your job search. Many companies use them to screen candidates, particularly for entry-level positions.

Popular AI certifications like Google's Professional Machine Learning Engineer and the Microsoft Azure AI Engineer Associate can boost your resume's appeal. But these are platform-specific and require some foundational knowledge.

The professional data science certification from 365 Data Science is an industry-recognized credential that prepares you for an AI career. It offers a comprehensive curriculum, covering everything from coding basics to machine learning algorithms. This certification is ideal for both newcomers and those seeking to enhance specific in-demand skills.

The primary benefit of certifications is that they demonstrate to potential employers your structured knowledge and commitment to the field.

This can be particularly advantageous if you're a beginner, opting for self-study, or transitioning from another industry into AI.

Remember, though, that the AI field evolves rapidly, so staying up to date is crucial. Even seasoned AI professionals can benefit from certifications—they provide an opportunity to enhance existing skills by focusing on specific, cutting-edge topics.

Self-Learning and Online Courses

In today's rapidly evolving AI field, self-learning and online courses have become increasingly common and accepted by the industry.

Platforms like 365 Data Science offer comprehensive AI and machine learning courses from expert instructors who have worked at top companies, making high-quality AI education accessible to a global audience.

This democratization of knowledge has not only leveled the playing field for aspiring AI professionals but also enabled experienced practitioners to continuously upskill, ensuring they remain competitive.

These online resources also provide a valuable alternative for those looking to enter the AI field without returning to traditional education, offering a more accessible and often more affordable path to gaining the necessary skills and knowledge.

To help guide your course selection based on your current skill set, here's an overview of the most sought-after skills for AI specialists.

Essential Skills and Tools for AI Careers

If you’re wondering about how to get into AI, our research on 1,000 machine learning engineer job listings has shown you need a mix of technical skills and familiarity with key tools.

Here are the most in-demand skills and tools:

Mastering these skills and tools will significantly boost your attractiveness to employers. Remember, the AI field evolves rapidly, so staying updated with these technologies is crucial for long-term success in your AI career.

Building a Strong AI Portfolio

A strong AI portfolio showcases practical projects that solve real-world problems.

Most employers seek candidates with work experience. If you lack this, projects are an excellent way to demonstrate your real-world skills.

While learning, tackle as many projects as you can, both large and small. When curating your portfolio, however, prioritize quality over quantity. Showcase a select few robust projects rather than many small ones.

Demonstrate your versatility in AI skills. Feature projects that utilize various techniques like machine learning, deep learning, NLP, and computer vision. Employers value a diverse skill set.

Thorough documentation is crucial. Utilize README files, code comments, and Jupyter notebooks to provide step-by-step explanations of your process. The main page for each project in your AI portfolio should include the following:

• Clearly define the problem
• Show your methodology: how you prepared data, selected models, and evaluated results
• Highlight how your AI-based solution adds value to an organization

It's essential to illustrate how your AI projects address—or have the potential to address—genuine challenges.

Emphasize your problem-solving approach and enhance your presentation with visuals—graphs and charts can effectively communicate complex concepts and make your work more engaging.

365 Data Science has numerous ML and AI-related projects which you can start working on today, taking away the difficulty of designing a project and searching for data sets. Explore some of these great projects to boost your AI portfolio:

• Music Genre Classification
• Tracking User Engagement with SQL, Excel, and Python
• Machine Learning for User Classification Project
• Customer Segmentation in Marketing with Python
• Regression Analysis in Excel

Experience and Internships

Figuring out how to work in AI can feel like a catch-22 situation—you need experience to get a job, but you need a job to gain experience. Fortunately, there are several effective ways to acquire practical experience in AI, even as a beginner.

Here are three methods to build your AI experience:

1. Secure an internship
2. Develop personal projects
3. Contribute to open-source initiatives

Internships at tech companies are particularly valuable for gaining hands-on experience in AI. These opportunities offer numerous benefits:

• Exposure to diverse projects and cutting-edge technologies
• Collaboration with experienced professionals in the field
• Insight into real-world applications of AI in business contexts
• Opportunity to identify your areas of interest within AI
• Potential for networking and future job prospects

By participating in an internship, you'll not only gain practical skills but also develop a better understanding of the AI industry landscape. This experience can shape your future AI career path by helping you identify which aspects of the field captivate you most and which sectors align with your interests and goals.

Looking for more tips about how to land an internship? Check out our YouTube video for insights and strategies:

How to Land a Data Analyst Internship with No Experience

Now that you have the skills, portfolio, and experience, the next part of our How to Get a Job in AI guide discusses landing your first AI role.

The Job Application Process

Securing a position in the AI field involves a demanding application process. Here's a detailed breakdown of what you can expect:

1. Resume Screening

The journey begins with resume screening, often conducted using AI tools. To increase your chances of progressing:

• Optimize your resume with relevant keywords
• Highlight your AI and ML skills prominently
• Showcase your most impactful projects and experiences

2. Technical Assessment

If your resume passes the initial screening, you'll likely face a technical assessment or coding challenge. This stage evaluates:

• Proficiency in AI and ML concepts
• Problem-solving abilities
• Coding skills in relevant languages (e.g., Python, R)

3. Initial Interview

Following the technical assessment, you'll typically have a phone or video interview with HR or a recruiter. They'll assess:

• Cultural fit within the organization
• Qualifications and experience
• Understanding of the role and company

During this interview, be prepared to:

• Discuss your background and how it relates to AI
• Explain your motivation for pursuing this role
• Ask thoughtful questions about the company and position
• Highlight any relevant certifications, projects, or AI skills

4. Technical Interviews

The core of the process involves multiple rounds of technical interviews with AI engineers and data scientists. Be prepared to:

• Discuss algorithms and machine learning concepts in depth
• Explain your past projects and methodologies
• Solve real-time coding problems or AI-related challenges

5. Take-Home Project

Some companies include a take-home project as part of the assessment. This might involve:

• Building an AI model from scratch
• Solving a real-world problem using AI techniques
• Analyzing and interpreting complex datasets

The take-home project can often take a few days to complete, so be prepared to dedicate some time to this section of the application.

6. Final Interview

The last stage typically involves an on-site interview (or its virtual equivalent). This may include:

• Meeting with the team and potential colleagues
• Presenting your take-home project or past work
• Discussing your potential role within the company

The entire process can span 4 to 6 weeks on average. While it's challenging, remember that AI salaries are competitive and the jobs themselves offer exciting career prospects.

The key to success is thorough preparation and the ability to demonstrate your skills effectively at each stage.

To help you prepare, consider reviewing our AI interview questions guides for AI researchers and AI engineers. These resources will familiarize you with the types of questions you might encounter during your interviews.

Transitioning into AI

As AI transforms the job market, many of you might be considering how to start a career in artificial intelligence while already in an established tech or non-tech role.

Transitioning into AI from a different background may require extra effort, but it's an achievable goal.

The key is to leverage your existing skills and experiences, no matter your previous work. Here's how you can make the transition smoother:

Identify Your Transferable Skills

Many skills are valuable in AI, even if they're not directly related. For example:

• Strong analytical skills from fields like finance or research
• Problem-solving abilities from engineering or scientific backgrounds
• Programming experience from software development
• Data handling skills from data analytics or statistics

Build on Your Tech Skills

Most jobs in AI require strong programming skills, especially in Python. If you're already in tech, you have a head start. If not, focus on:

• Learning Python programming
• Understanding data structures and algorithms
• Familiarizing yourself with data manipulation libraries like pandas

Our Python Programmer Bootcamp is a great place to start. And if you already have that foundation, explore our more specialized courses like Data Cleaning and Preprocessing with pandas.

Learn AI/ML Fundamentals

Focus on key AI/ML concepts and tools. Refer to the AI job requirements above for an idea of what you need to know.

• Master supervised and unsupervised learning, deep learning architectures, and natural language processing.
• Get an understanding of large language models, computer vision algorithms, and other specialized skills.
• Familiarize yourself with AI frameworks like TensorFlow or PyTorch.

Our Machine Learning in Python course is a comprehensive guide through foundational machine learning techniques that will take your data analysis skills to the next level.

Leverage Domain Expertise

Your existing industry knowledge is valuable. For instance:

• Financial expertise can be applied to AI in fintech
• A healthcare background is crucial for AI applications in medicine
• Retail experience can inform AI-driven customer analytics

Network, Network, Network

Expand your professional network in the AI field:

• Join AI meetups and conferences
• Participate in online AI communities and forums
• Follow AI thought leaders on social media

Be Open to Different Entry Points

Consider starting in related roles and working your way into an AI career:

• Data analyst positions as a stepping stone
• Software engineering roles in AI-focused teams
• Junior AI or machine learning positions

Remember, transitioning into AI is a journey. Be patient, persistent, and always eager to learn. With the right approach and dedication, you can successfully pivot your career.

How to Get a Job in AI with 365 Data Science

The rapid growth in AI signals a promising future for careers in this field. The key takeaway from this article is that you can join an AI career from any starting point—without starting from scratch. Thanks to online resources, launching an AI career is now more accessible than ever.

At 365 Data Science, we offer a comprehensive learning platform aligned with the key areas highlighted in this article:

• Foundational skills in Python, statistics, and mathematics
• AI and ML fundamentals, including machine learning algorithms and deep learning
• Practical projects simulating real-world AI applications
• Interview preparation resources

Our platform is flexible, allowing you to learn at your own pace and style. By enrolling, you're preparing for a successful career transition into AI.

Remember, mastering AI is a lifelong journey. One of the most important elements of how to get a job in AI is staying up to date with the field’s latest developments. By continuously expanding your knowledge and skills, you'll ensure your long-term success.

FAQs