How to Get a Job in AI Without a Degree

Key takeaways

You can get a job in AI without a degree by building in-demand skills, gaining practical experience, and creating a strong portfolio.

• Data shows that 70 percent of employers rely on skills-based hiring, which places focus on relevant skills rather than a degree [1].

• Popular programming languages for AI jobs include Python, R, C++, and Java.

• You can work in various AI-related roles without a degree, including in roles like data analyst, prompt engineer, and AI content writer.

Learn more about how to get a job in AI without a degree, including the alternative learning options and a few roles to consider. If you’re ready to begin, consider enrolling in the IBM AI Developer Professional Certificate. This beginner-friendly 10-course series can help you become career-ready by learning key concepts and terms, building generative AI applications, and earning a valuable career credential to demonstrate your expertise to employers.

No degree needed? AI’s rapid growth fuels skills-based hiring

As artificial intelligence (AI) continues to transform global industries, its rapid growth also introduces diverse career opportunities across industries such as healthcare, finance, manufacturing, and technology. In addition to creating opportunities, it has also led to a global skills shortage while paving the way for a growing skills-based hiring strategy, making it even more possible to get a job in AI without a degree, provided you have the right skills. 

A survey from the National Association of Colleges and Employers (NACE) reveals that 70 percent of employers use skills-based hiring, a percentage which has increased 5 percent since 2025 [1]. This hiring practice prioritizes your skill set over a degree, mitigating the potential impact of not having one. For example, research from the World Economic Forum reveals that candidates with AI skills are 8 to 15 percent more likely to receive an interview, even when all other resume details are identical to those of their competitors [2].

This means that you can pursue various AI jobs without a degree, focusing on your practical skills and experience, both of which you can build through alternative pathways. This is particularly true for entry-level roles, like prompt engineers and AI research assistants. It also applies to various other positions, including data annotator, deep learning engineer, AI trainer, AI writer or editor, and software engineer, which you may qualify for after completing online courses, certifications, coding bootcamps, and projects. For individuals without traditional academic credentials, developing technical expertise in programming, data analysis, and AI tools can open doors to exciting and lucrative careers in AI.

How to get a job in AI without a degree

Getting a job in AI without a formal degree is possible. Focusing on your skills and practical experience can help you become competitive. Additionally, because jobs in AI require you to follow a set of ethical standards to ensure privacy and fairness of data for training and creating models, becoming proficient in AI ethics can also offer a solid starting point.

To begin, consider these steps: 

1. Build your knowledge base and skills.

2. Work on projects to gain practical experience.

3. Become familiar with relevant programming languages and AI tools.

4. Develop your professional network.

5. Prioritize your online presence.

6. Apply for entry-level AI jobs.

What skills do you need to get an AI job without a degree?

In general, you should consider mastering foundational skills such as programming (i.e., Python, R, or Java), data analysis, and working with algorithms, as well as core concepts like machine learning, neural networks, and data science. However, it’s also helpful to focus on developing the core skills essential for the AI job you want to pursue. 

Many AI roles fall into several broad groups, each defined by specific skills, responsibilities, and focus areas within the AI ecosystem. For example, if you want to become a data analyst, you would need to know how to collect and clean data in addition to becoming familiar with using AI tools to aid in data collection, evaluation, interpretation, and visualization. 

However, if you want to pursue a career in AI engineering, developing new systems and applications that use AI, you’ll need strong analytical skills in addition to the ability to use machine learning techniques for tasks such as image analysis and complex problem-solving, and the ability to develop AI-driven solutions. 

Read more: Navigating Your Artificial Intelligence Career Path

Can I work in AI without a degree?

Yes. But first, you need to sharpen your skills and build your AI knowledge. To gain foundational knowledge outside of a degree program, you might consider online programs, which offer flexible learning, or boot camps, which offer intensive study in a short timeframe. In both instances, you’ll likely have opportunities to work on portfolio-building projects to gain valuable practical experience.

How can you gain AI experience without a degree?

Working on personal or open-source projects is a highly effective way to showcase your skills and stand out to potential employers in the AI field. Employers increasingly value demonstrable expertise over formal qualifications, and a portfolio of projects acts as tangible proof of your abilities.

Start by browsing beginner-friendly issues on GitHub labeled “good first issue” for topics designed for beginners. To build your portfolio, consider contributing to and collaborating on open-source projects on platforms like GitHub and GitLab. You might also work on independent projects using open-source frameworks such as TensorFlow, PyTorch, and scikit-learn. Focus on selecting projects that align with your career goals. 

What programming languages should you learn for AI?

To succeed in AI, mastering key programming languages and tools is essential. These technologies form the foundation for developing and implementing AI systems, enabling AI professionals to analyze data, build models, and deliver effective solutions to real-world problems.

Start by learning popular programming languages in AI, such as: 

• Python: Python can help you with continuous data processing, implement complex algorithms, and collaborate with others, while offering powerful libraries for programming tasks, including scikit-learn for machine learning, TensorFlow for neural networks and deep learning, and Keras for prototyping deep learning models. 

• R: Developed for data handling, R supports interactivity between AI and data analysis, visualization, and machine learning. It’s particularly well-suited to projects like predictive modeling and sentiment analysis, and offers access to various libraries, including caret and mlr for developing models. 

• C++: C++ offers speed and efficiency for rapid AI code execution, machine learning application development, and large language model development. 

• Java: Java is an object-oriented programming language known for its speed, scalability, and reliability, ideal for AI projects requiring fast execution and for building enterprise-level applications.

Popular AI tools and frameworks include the following: 

• TensorFlow: TensorFlow is a versatile framework that supports languages such as Python and JavaScript, enabling AI professionals to build and deploy machine learning models across platforms. 

• Keras: An open-source Python library known for its user-friendly, modular design, enabling quick prototyping of deep learning models. With an intuitive, high-level API, it’s an ideal framework for beginners and advanced users alike.

• scikit-learn: This open-source Python library offers scalable machine learning algorithms for supervised and unsupervised tasks. It’s known for its simplicity, reusable components, active community, and for data mining and analysis.

• PyTorch: PyTorch is an open-source AI framework with an intuitive interface, flexible model building, and efficient graphics processing unit (GPU) acceleration. It excels at rapid prototyping and deep learning research.

Network with industry professionals

Making human connections is arguably more important than ever, and networking is an effective tool for building the connections that can help open doors to job opportunities, mentorship, and collaborations. The AI industry thrives on shared knowledge and innovation, and engaging with professionals helps you stay up to date on trends, tools, and best practices.

Begin building your network by leveraging opportunities such as: 

• Joining and participating in AI communities on LinkedIn or forums on Reddit 

• Attending in-person AI-focused events, meetups, and conferences 

• Contributing to open-source projects 

Consider earning a certification or other credentials 

If you’re just starting your career or pivoting from another field, earning an educational credential or a certification in AI can help showcase your skills and stand out among other candidates. These credentials help validate that you have the skills you’ll need in many AI jobs so that you can go on to pursue your career. 

Elevate your resume and professional profile with certificates and certification programs like: 

• IBM Machine Learning Professional Certificate 

• United States Artificial Intelligence Institute (USAII) Certified AI Transformation Leader

• Microsoft Certified: Azure AI Engineer Associate 

• Google Professional Machine Learning Engineer Certification 

• AI CERTs AI+ Prompt Engineer Level 1 

Showcase your skills on online platforms

Platforms such as GitHub and LinkedIn are useful tools for demonstrating your skills, sharing projects, and attracting potential employers. GitHub, a popular platform for hosting and sharing AI projects, allows you to showcase your coding and problem-solving abilities through projects. LinkedIn, a professional social media platform, is another way to showcase your skills while networking with other AI professionals. A strong online presence demonstrates expertise, reflects professionalism, and attracts opportunities that can lead to referrals and job offers. 

Apply for entry-level positions 

You might find success breaking into the field of AI by starting with entry-level positions that don’t typically require a degree. Starting in an entry-level position has many advantages. You can learn from experienced professionals, get connected in the world of AI, and gain invaluable skills and real-world experiences to add to your resume. 

AI jobs without a degree: Roles to consider 

Many AI jobs do not require a formal degree. It’s common for employers to prefer a degree in some cases (i.e., in high-level positions), but you can take other paths to fulfill education requirements. When looking for AI jobs without a degree, search job listings on popular sites such as Indeed, Monster, or ZipRecruiter. Use the filters to narrow your choices and find jobs that match your qualifications.

Consider some of the following AI jobs that you may be able to break into without a degree, and learn how much you can expect to earn in each position.

All salary information is the median total pay from Glassdoor as of May 2026. These figures include both base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other forms of compensation.

Data analyst 

Median total pay: $93,000 [3] 

Requirements: Strong foundation in data analytics skills, including statistical analysis techniques and the ability to use data visualization tools; some employers may require a bachelor’s degree, but it’s not required. 

A data analyst uses AI tools and data analysis techniques to solve problems and answer routine operational questions. In this role, you’ll also identify key insights into a company’s customer base and communicate findings to stakeholders and decision makers in an organization. 

Machine learning (ML) engineer

Median total pay: $162,000 [4] 

Requirements: You don’t need a degree, though if you want to work in leadership roles, a degree can help you achieve future career goals. You will need extensive knowledge of programming languages, strong mathematical skills, proficiency in problem-solving, and proficiency in data analysis. 

Machine learning engineers create software for machine learning applications. In this role, you’ll develop and work with machine learning algorithms and models, build application programming interfaces, perform testing, handle implementation, and oversee documentation for the software you create. 

AI software engineer

Median total pay: $169,000 per year [5]

Requirements: A degree can be helpful, but it is not necessary. You’ll need proficiency in machine learning and deep learning, data engineering and management, statistical and mathematical knowledge, programming and software development, and cloud computing and infrastructure. 

AI software engineers create applications and systems that use artificial intelligence to enhance efficiency, improve decision-making, reduce costs, and boost profits. In this role, you will integrate AI principles to address uncertainty, applying machine learning techniques for tasks such as image analysis, and developing solutions that mimic human behavior to tackle complex processes. You’ll also combine AI best practices with machine learning, evaluate and optimize system performance, and solve intricate problems using advanced AI techniques. 

What is the easiest AI job to get?

Although you won’t find one single “easiest” or “best” AI job, prompt engineering can offer an excellent entryway into working in AI. It comes with a median total pay of $130,000 [6] and offers multiple paths to enter the field. The work typically involves developing instructions for large language models, frequently testing and refining prompts to help the AI understand the question or task. In doing so, you help AI better understand and respond to human language, ultimately enhancing the quality of the results it delivers.

AI content specialist

Median total pay: $112,000 [7]

Requirements: Some employers may prefer a bachelor’s degree in English or a related field to work as an AI content specialist; you will need strong writing, editing, and digital marketing skills, experience with AI writing tools and technologies, and an understanding of SEO best practices. 

AI content specialists (also referred to as AI writers) use AI writing software to enhance the efficiency, accuracy, and creativity in various types of content creation. As an AI writer, you would likely use AI software to help with tasks such as generating blog content, crafting marketing emails, creating social media posts, and writing video scripts. You might also use AI as a tool for research, ideation, and organization, helping start your projects before you verify and refine the results and add personal details for quality and authenticity.

Explore our free AI resources to continue learning

Get fresh insights and career guidance with a subscription to Career Chat, our weekly LinkedIn newsletter. Then, check out our additional resources to continue building your AI knowledge and skills.

• Watch on YouTube: Artificial Intelligence Jobs Unlocked: Pathways to the Future

• Find your career path: AI Career Quiz: Is It Right for You? Find Your Role

• Hear from an expert: 6 Questions with an IBM Data Scientist and AI Engineer

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