How to Become an AI Developer: Education, Skills, Salary & Career Path

Artificial intelligence has created a new category of software development in which programmers build applications that can understand language, generate content, analyze information, make predictions, and interact with users in ways that traditional software cannot.

That has made the AI developer an increasingly important role across technology and business. But becoming an AI developer is not simply a matter of learning how to use ChatGPT or another generative AI tool. Employers need people who understand software development, programming, APIs, data, AI models, testing, deployment, and the practical limitations of AI systems.

The career can also look different depending on the employer. One AI developer might build generative AI applications using large language models and APIs. Another might integrate machine-learning models into an existing software product. Others may work alongside data scientists and machine-learning engineers to move AI systems into production.

The broader software development market remains strong. The U.S. Bureau of Labor Statistics projects 15% employment growth for software developers, quality assurance analysts, and testers from 2024 to 2034, with about 129,200 openings per year on average. Software developers had a median annual wage of $133,080 in May 2024.

For professionals considering an AI career, the opportunity is increasingly about combining software engineering fundamentals with AI expertise rather than choosing between the two.

What Does an AI Developer Do?

An AI developer designs, builds, integrates, tests, and maintains software applications that use artificial intelligence.

The role sits at the intersection of software development and AI. Depending on the organization, an AI developer may work with pre-trained models and APIs rather than creating new AI models from scratch.

Typical responsibilities can include:

• Building AI-powered applications
• Integrating AI models and APIs into software
• Developing chatbots and virtual assistants
• Writing Python, JavaScript, or other programming languages
• Working with generative AI and large language models
• Developing prompts and AI workflows
• Connecting applications to databases and external services
• Testing AI outputs
• Improving application performance
• Working with machine-learning models
• Deploying AI applications
• Monitoring applications after deployment
• Collaborating with software engineers, data scientists, product managers, and business teams

O*NET's software developer occupation includes responsibilities such as analyzing user needs, developing software solutions, updating existing software, and applying computer science, engineering, and mathematical principles to software development.

The important distinction is that AI development is still software development. The AI component adds another layer of technical knowledge rather than eliminating the need for traditional programming skills.

Why AI Developers Matter to Businesses

Businesses increasingly want to move beyond experimenting with AI tools and actually incorporate AI into products, services, and internal workflows.

That creates a need for professionals who can translate an AI capability into working software.

For example, a company might have access to a powerful language model but still need an AI developer to build the application around it, connect it to company data, create authentication and user interfaces, evaluate responses, and deploy the system securely.

This distinction helps explain why AI development should not be viewed simply as "prompt engineering."

The valuable skill is knowing how to build something useful with AI.

PwC's 2026 Global AI Jobs Barometer, which analyzed more than one billion job advertisements across 27 countries and territories, found that jobs requiring specific AI skills were growing substantially faster than the overall jobs market. PwC also reported an average 62% wage premium for workers with AI skills, although that figure represents an advertised-pay comparison and does not control for factors such as education, experience, or location.

Step 1: Learn Software Development Fundamentals

The first step toward becoming an AI developer is learning how software is actually built.

Someone can learn how to call an AI API relatively quickly. Building a reliable application around that API requires considerably more knowledge.

Important foundations include:

• Programming
• Data structures
• Algorithms
• APIs
• Databases
• Version control
• Web development
• Software testing
• Debugging
• Application architecture
• Cloud computing
• Security fundamentals

Python is particularly useful because it is widely used throughout AI and machine learning. O*NET's 2025 employer-posting data for software developers also shows Python among the most frequently requested software skills, along with AWS, Java, SQL, JavaScript, Azure, Kubernetes, Git, REST APIs, React, and Docker.

This is one reason I would not recommend approaching AI development as a shortcut around learning programming.

Step 2: Develop AI and Machine Learning Skills

Once the software foundation is in place, the next step is understanding how AI systems work.

An aspiring AI developer should become familiar with concepts such as:

• Machine learning
• Neural networks
• Generative AI
• Large language models
• Natural language processing
• Prompt engineering
• Model evaluation
• Fine-tuning
• Retrieval-augmented generation
• Embeddings
• Vector databases
• AI APIs
• AI application architecture

Not every AI developer needs to become a machine-learning researcher.

The goal is to understand enough about AI to select appropriate technologies, integrate models into applications, evaluate their performance, and recognize their limitations.

Step 3: Build AI Projects

This is where the career path becomes much more practical.

Employers need evidence that a candidate can build things—not simply complete courses.

An aspiring AI developer could create projects such as:

• An AI customer-service chatbot
• A document-question answering application
• A meeting summarization tool
• An AI-powered search application
• A sentiment-analysis application
• A personal productivity assistant
• A retrieval-augmented generation application
• An AI application that uses external APIs
• A voice-enabled AI assistant
• An AI application connected to a private knowledge base

A portfolio can demonstrate programming ability, AI integration, problem-solving, user experience, and an understanding of how AI applications actually work.

It also gives candidates something concrete to discuss during technical interviews.

Skills Employers Look For in AI Developers

The AI developer skill set is broader than knowledge of AI models.

Programming skills

Python is particularly important, but depending on the role, employers may also expect JavaScript, Java, C#, C++, TypeScript, or other languages.

O*NET's current employer-posting data for software developers shows Python, AWS, Java, SQL, JavaScript, Azure, Kubernetes, Git, REST APIs, React, Docker, and C# among frequently requested technologies.

AI and machine-learning skills

AI developers should understand the fundamentals of machine learning and generative AI, including how models are selected, integrated, tested, and evaluated.

API and application development

Modern AI applications often depend on APIs. Understanding how applications communicate with external services is therefore an important practical skill.

Data skills

AI applications frequently interact with databases, documents, structured data, and unstructured information.

Understanding how data is stored, retrieved, cleaned, and secured is valuable.

Cloud and deployment skills

AI applications need to operate somewhere. Familiarity with cloud platforms, containers, deployment pipelines, and monitoring can distinguish a developer who can build a demonstration from one who can help deliver a production application.

Communication and judgment

AI developers increasingly work across technical and business teams.

They need to understand what a business is trying to accomplish and determine whether AI is actually the right solution.

That human judgment is becoming more important as AI becomes easier to access. PwC's 2026 research found that the most AI-exposed junior roles were seven times more likely to require traditionally senior skills such as judgment and leadership.

AI Developer Career Path

There is no single progression for AI developers. Some begin as software developers and specialize in AI. Others enter through data science, machine learning, or computer science education.

A typical progression might look like this:

Junior Software Developer / AI Developer
Build programming fundamentals and contribute to AI-enabled applications.

AI Developer / Software Developer
Build and integrate AI features into production applications.

Senior AI Developer / AI Engineer
Take responsibility for architecture, model integration, performance, and more complex AI systems.

Staff or Principal AI Engineer
Lead technical architecture and major AI initiatives across teams.

AI Architect / Engineering Lead
Define enterprise AI architecture, platforms, standards, and technical strategy.

AI Engineering Manager / Director
Lead teams, budgets, technology strategy, and delivery.

There are also lateral opportunities into machine learning engineering, data science, solutions architecture, AI product management, or technical consulting.

AI Developer Career Path Comparison

These are broad career patterns rather than formal requirements. Titles and experience expectations vary significantly between employers.

Salary: How Much Does an AI Developer Make?

There is no single government salary category for "AI developer," which makes salary estimates more complicated than they are for established occupations.

AI developers may be classified as software developers, software engineers, machine-learning engineers, data scientists, or other technology professionals.

As a useful benchmark, the BLS reports a $133,080 median annual wage for software developers as of May 2024. The highest 10% earned more than $211,450. The BLS also projects 15% growth for software developers, quality assurance analysts, and testers from 2024 through 2034.

O*NET's 2025 data puts the median wage for the broader software developer occupation at $135,980, with 1.69 million workers employed in 2024 and projected growth classified as "much faster than average."

For AI-specific roles, compensation can be substantially higher depending on specialization, experience, employer, location, and the scarcity of particular technical skills.

PwC's 2026 AI Jobs Barometer found an average 62% wage premium for jobs requiring AI skills compared with similar jobs without those skills. This is a broad AI-skills measure rather than a salary guarantee for AI developers.

Job and Career Growth Opportunities

The strongest reason to consider AI development is not simply the number of AI job titles appearing in job boards. It is the increasing integration of AI into existing software and business processes.

Software developers are already working across industries, and O*NET data shows that 40% of software developers are employed in professional, scientific, and technical services, 20% in information, and 10% in finance and insurance, with the remainder distributed across industries including manufacturing and management.

AI expands the potential applications within those industries.

Professionals may work on:

• Generative AI applications
• Enterprise AI platforms
• AI-powered customer experiences
• Intelligent automation
• Recommendation systems
• Document processing
• Predictive applications
• Computer vision
• Conversational AI
• AI search
• Internal knowledge systems
• AI-enabled software products

Industries That Hire AI Developers

Technology

Technology companies remain a major employer of AI and software talent, particularly for AI products, developer platforms, cloud services, and enterprise applications.

Financial Services

Banks, insurers, investment firms, and fintech companies use AI for fraud detection, customer service, risk analysis, automation, and financial applications.

Healthcare

Healthcare organizations use AI for administrative automation, clinical applications, research, patient engagement, and information management.

Manufacturing

Manufacturers increasingly use AI for predictive maintenance, quality control, robotics, forecasting, and supply chain optimization.

Retail and E-Commerce

AI developers can work on recommendation engines, search, personalization, customer service, demand forecasting, and inventory systems.

Professional Services

Consulting, accounting, legal, and other professional-service organizations increasingly need people who can build or integrate AI solutions into business workflows.

Government and Defense

Government agencies use AI in areas ranging from cybersecurity and logistics to research, public services, and national security.

Education Requirements for AI Developers

A bachelor's degree in computer science, software engineering, information technology, mathematics, data science, or a related field is a common route into software development.

The BLS states that software developers, quality assurance analysts, and testers typically need a bachelor's degree in computer and information technology or a related field, although employer preferences can vary.

O*NET's employer data similarly shows that 85% of respondents identified a bachelor's degree as the required education for new hires in the software developer occupation.

However, AI development is also a field where demonstrated technical ability can matter significantly.

A career changer may build relevant skills through professional certificates, programming education, projects, internships, open-source work, and other practical experience.

The more technically demanding the position, the more important it becomes to demonstrate genuine software engineering and AI capability.

Certifications and Professional Certificates That Can Help

Professional certificates are not a substitute for a computer science degree when a specific employer requires one. They can, however, provide structured training and help career changers or existing developers build targeted AI skills.

The three programs below fit different points on the AI development path.

IBM AI Developer Professional Certificate

The IBM AI Developer Professional Certificate is the most accessible of the three recommendations for someone beginning an AI development journey.

Coursera lists it as a beginner-level, 10-course series with a flexible schedule. The program covers software engineering fundamentals, AI, generative AI, prompt engineering, HTML, JavaScript, and Python. It also includes hands-on projects such as building AI applications, a sentiment-analysis application, a ChatGPT-like application, a voice assistant, and other AI projects.

That practical component is particularly relevant for someone who needs to start building a portfolio rather than simply collecting course completions.

Best suited for: Beginners and career changers who need a structured introduction to programming and AI application development.

Learn More About the IBM AI Developer Professional Certificate on Coursera

IBM AI Engineering Professional Certificate

The IBM AI Engineering Professional Certificate is a more advanced option.

Coursera describes it as an intermediate-level, 13-course series designed around AI engineering skills. Topics include machine learning, deep learning, neural networks, supervised and unsupervised learning, PyTorch, TensorFlow, Keras, Apache Spark, LLM applications, fine-tuning, retrieval-augmented generation, and generative AI.

This makes it more appropriate for someone who already has programming or software development fundamentals and wants to move deeper into AI engineering.

Best suited for: Developers and technical professionals who already have programming experience and want more advanced AI and machine-learning skills.

Learn More About the IBM AI Engineering Professional Certificate on Coursera

IBM Generative AI for Software Developers Specialization

The IBM Generative AI for Software Developers Specialization takes a somewhat different approach.

Rather than serving as a broad introduction to AI engineering, it focuses on applying generative AI to software development. The three-course specialization covers generative AI fundamentals, prompt engineering, and the use of tools such as GitHub Copilot, ChatGPT, and Google Gemini for software development tasks.

This makes it particularly relevant to software developers who already understand programming and want to incorporate generative AI into their development workflow.

Best suited for: Existing and aspiring software developers who want to add generative AI development skills.

Learn More About the IBM Generative AI for Software Developers Specialization on Coursera

What Employers Look For in an AI Developer

The most competitive candidates will generally be able to demonstrate more than familiarity with AI terminology.

Employers want evidence that a candidate can actually build and maintain software.

That means a strong candidate might demonstrate:

• Programming proficiency
• AI application development
• Git and version control
• API integration
• Database knowledge
• Cloud experience
• Testing and debugging
• Understanding of AI limitations
• Portfolio projects
• Ability to work with other developers
• Ability to explain technical decisions

This is particularly important because AI tools can now make it easier for people to produce code without fully understanding it.

Research into AI-enhanced software development has found that effective developers still need a combination of generative-AI skills, core software engineering knowledge, adjacent engineering skills, and non-engineering capabilities.

How AI Is Changing the AI Developer Role

There is an obvious paradox: AI developers are helping build technology that can automate parts of software development.

But that does not necessarily make the profession less important.

Instead, the nature of the work is changing.

Developers increasingly use AI coding assistants to generate code, explain unfamiliar code, identify bugs, write tests, and accelerate routine development tasks.

That shifts more attention toward architecture, requirements, testing, security, evaluation, system design, and judgment.

Research examining job postings for generative-AI roles has also found higher requirements for cognitive and social skills, suggesting that AI-heavy work can increase rather than eliminate the importance of broader professional capabilities.

For an aspiring AI developer, the lesson is straightforward: learn how to use AI without losing the ability to understand the underlying software.

Is AI Development a Good Career?

AI development can be an attractive career for people who enjoy programming, technology, problem-solving, and continuous learning.

The potential advantages include:

• Strong software-development employment outlook
• Opportunities across many industries
• Multiple technical specialization paths
• High earning potential
• Exposure to rapidly developing technology
• Opportunities to work on new products and applications

There are also challenges.

AI developers need to continually update their skills. Tools and frameworks change quickly, competition for desirable technical positions can be intense, and employers may expect candidates to demonstrate practical experience rather than simply complete courses.

The profession is also becoming more demanding at the entry level. PwC's research suggests that AI-exposed junior roles increasingly require skills traditionally associated with more experienced workers, including judgment and leadership.

That makes building real projects, understanding software fundamentals, and developing communication skills increasingly important.

Building a Career as an AI Developer

There is no single route into AI development, but a practical progression looks something like this:

1. Learn programming fundamentals.

Start with Python and develop a solid understanding of software development.

2. Learn how AI works.

Study machine learning, generative AI, APIs, LLMs, and AI application architecture.

3. Build real projects.

Create applications that demonstrate what you can actually do.

4. Develop modern development skills.

Learn Git, APIs, databases, cloud platforms, testing, and deployment.

5. Add a professional certificate if it fills a skills gap.

Choose a program based on your current experience rather than simply selecting the most advanced credential.

6. Build a portfolio.

Use projects to demonstrate your technical ability to potential employers.

7. Continue learning.

AI development is not a career where education ends after your first job. New models, frameworks, tools, and development practices will continue changing the profession.

Start Building Your AI Development Career

Becoming an AI developer is less about finding a shortcut into artificial intelligence and more about combining two valuable capabilities: the ability to build software and the ability to apply AI effectively.

For beginners, that may mean starting with programming and AI application development. For experienced software developers, it may mean moving deeper into machine learning, generative AI, model integration, or AI architecture.

Professional certificates can help structure that learning, but the strongest career strategy is to combine education with practical experience. Build applications. Learn how APIs and models work. Understand the limitations of AI. Develop your software engineering fundamentals. And create a portfolio that gives employers evidence of what you can actually build.

That combination is likely to remain valuable even as the technology itself continues to change.

Continue Your Professional Development

Ready to build the skills employers value? Explore professional development opportunities, online courses, professional certificates, and executive education from leading universities, technology companies, and trusted training providers.

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About the Business Training Media Editorial Team

This article was researched and written by the Business Training Media Editorial Team. We publish expert content covering business strategy, leadership, workplace skills, artificial intelligence, cybersecurity, compliance, career development, online learning, professional certifications, business software, and organizational excellence. Our goal is to provide practical, research-backed insights that help professionals, business leaders, and organizations make informed decisions.