Artificial intelligence (AI) is rapidly transforming industries from healthcare and finance to marketing and tech, creating tons of new job opportunities. The good news for anyone wondering how to get a job in AI is that there are plenty of ways to break into the field even without a degree in AI.
So, how do you start? What skills do you need, and how can you position yourself for success in this exciting industry? We’ve got all the details to help you take your first steps toward a career in AI.
Understanding the AI landscape
AI encompasses a broad range of technologies, from machine learning (ML) and deep learning (DL) to natural language processing (NLP) and computer vision. These technologies are used to create systems that can perform tasks that traditionally require human intelligence, such as recognizing speech, interpreting images, and learning from data.
With its growing importance, AI is rapidly expanding across industries and becoming a crucial part of the global economy. “AI no longer is a sole domain,” says Cache Merrill, founder of Zibtek, who has spent over a decade helping startups and tech companies build teams that push boundaries in AI and emerging tech. “If you have a passion for programming, data science, linguistics, designing, or even psychology, you have the opportunity to explore AI as your career.”
Different types of jobs in AI
Right now, you're probably wondering, “How can I get a job in AI?” The first step is knowing what kinds of roles are out there. The AI job that’s right for you will depend on your background, interests, and skill set.
“For example, if you’re more technical, roles like AI research scientist or NLP specialist might be for you,” says IT and cybersecurity specialist Matthew Kaing, CEO of eSudo Technology Solutions. “If you’re more interested in the business side of AI, AI product management or AI strategy consulting might be the way to go.”
Here are seven of the most common positions you might want to explore.
Data scientist
Data scientists work with large datasets, extracting valuable insights and building models using machine learning algorithms. They are typically proficient in programming languages such as Python, R, and SQL, and are experts in statistics and data analysis.
Machine learning engineer
ML engineers are responsible for building machine learning models and ensuring they are scalable and effective. They work closely with data scientists and software engineers to deploy AI solutions.
AI research scientist
AI researchers focus on advancing AI technologies by exploring new algorithms and methodologies. Their work often involves theoretical and applied research in areas such as deep learning, reinforcement learning, and NLP.
AI software developer
AI software developers design and build AI-powered applications, integrating machine learning and other AI technologies into software products. These developers must be proficient in coding and have a solid understanding of AI algorithms.
AI ethics specialist
As AI grows more pervasive, the ethical implications of AI systems are under intense scrutiny. AI ethics specialists are responsible for ensuring that AI systems are developed and deployed responsibly, addressing concerns like bias, transparency, and accountability.
Business intelligence analyst
Business intelligence analysts use AI tools to analyze data and help organizations make data-driven decisions. They focus on understanding trends and providing actionable insights based on data.
AI product manager
AI product managers oversee the development of AI-powered products, ensuring that AI solutions align with business objectives and customer needs. They must be well-versed in both AI technologies and project management principles. This role can be found in many sectors, including healthcare, finance, robotics, automotive, retail, and entertainment.
Key skills needed in AI
To build a strong skill set for AI you need a blend of technical expertise and essential soft skills. It’s not just about coding; you also have to think critically, collaborate, and communicate complex ideas. Here are some key skills you’ll want to develop if you’re aiming to land a job in AI.
Technical skills
- Programming: AI runs on code, so knowing languages like Python, R, Java, or C++ is a must.
- Machine learning: At the heart of AI, machine learning helps systems recognize patterns and make predictions. Understanding concepts like supervised (where models learn from labeled data) and unsupervised learning (where models find patterns in unlabeled data) gives you the foundation to build smarter AI models.
- Data science: AI is nothing without data. Knowing how to collect, clean, and analyze data helps you train better models and make accurate predictions.
- AI frameworks: Familiarize yourself with popular AI frameworks like TensorFlow, PyTorch, and Keras. Learning these tools makes model development and deployment much easier.
- Cloud computing: AI often requires a ton of computational power, and cloud platforms like AWS, Google Cloud, and Microsoft Azure provide the resources to train and deploy models at scale. Getting familiar with cloud-based AI tools can give you an edge, and many platforms offer free credits to help you experiment.
Soft skills
- Problem-solving: AI professionals need to be adept at breaking down complex problems and finding creative solutions.
- Communication: The ability to explain complex technical concepts to non-technical stakeholders is valuable in AI roles.
- Collaboration: Working in AI often involves collaboration with cross-functional teams, so strong teamwork skills are essential.
How to get a job in the AI field—with or without experience
So, how to land a job in AI? Whether you're a newcomer to tech or have experience in a related field, there are several steps you can take to start a career in AI.
If you’re starting out:
- Understand the basics. If you're wondering how to get a job in AI with no experience, you'll need to grasp the fundamentals. Numerous online resources, courses, and certifications can help you understand AI basics. Platforms like Coursera, edX, and Udacity offer courses on machine learning, data science, and AI programming languages.
- Focus on programming languages. AI professionals need strong technical skills. Understanding Python, R, and Java languages is common in AI development.
- Learn mathematics and statistics. A solid understanding of linear algebra, calculus, probability, and statistics is critical for working with machine learning algorithms.
- Study machine learning. Familiarize yourself with supervised and unsupervised learning, neural networks, and deep learning.
- Pursue formal education. Having a degree in a relevant field such as computer science, data science, or engineering can make you a more competitive candidate. Many universities now offer specialized programs in AI, machine learning, and robotics.
- Build a portfolio. One of the best ways to demonstrate your AI skills is through hands-on projects. Building a portfolio of AI-related projects shows potential employers that you can apply your knowledge in real-world situations. Contribute to open-source projects, create personal projects, or work on Kaggle competitions to enhance your portfolio.
- Stay up to date with AI trends. AI is a rapidly evolving field, and keeping up with the latest research, advancements, and industry news will help you stay competitive. Join AI-related communities, attend conferences, and follow influential researchers and companies.
Read this next: AI Skills: What You Need to Learn to Stay Ahead
If you have some tech experience:
- Leverage your existing skills. If you have experience in programming, data analysis, or software engineering, you'll be able to quickly adapt to AI technologies. For instance, machine learning is built upon programming skills and statistical analysis, so your background in these areas will be valuable.
- Develop specialized AI knowledge. Focus on learning the AI-specific tools and technologies that are in demand. Dive deeper into areas such as neural networks, natural language processing, and reinforcement learning (we will delve into this later).
- Take specialized courses. Consider pursuing advanced courses or certifications in machine learning and AI. These programs can help you build on your existing expertise and tailor your skills to AI development.
- Network and seek mentorship. The AI community is full of professionals eager to share their knowledge. Attend meetups, join online forums, and seek mentorship from industry experts. Building relationships with established AI professionals can help you learn faster and gain valuable insights into the field.
Read this next: 9 Tips to Turn Networking Contacts Into Meaningful Relationships
Prepare your resume for an AI job
When it comes to landing a job in AI, your resume should highlight your skills, experience, and projects that are most relevant to the field. Here are a few tips:
- Tailor your resume to AI roles. Focus on AI-related skills, including programming languages, machine learning experience, and any AI projects you've worked on.
- Showcase projects and achievements. If you've contributed to open-source AI projects or completed personal AI projects, be sure to include them on your resume. Use measurable results to show the impact of your work.
- Highlight certifications and education. Include any relevant degrees, courses, and certifications that demonstrate your commitment to learning about AI.
- Emphasize problem-solving. AI employers are looking for candidates who can approach problems creatively. Highlight instances where you solved complex issues or used AI technologies to drive results.
Make your move into AI
Whether you're just starting out or considering a career shift, breaking into the AI field is totally within reach with the right preparation and strategy. It’s all about being proactive, staying curious, and building both the technical and soft skills that matter.
The more interviews you land, the more options you’ll have—check open jobs on The Muse to find your next opportunity »