What Is an AI Architect?

Artificial intelligence (AI) is rapidly gaining traction worldwide, and many experts and leaders believe it could transform the global economy. Global professional services network PwC estimates that by 2030, AI will contribute $15.7 trillion to the world's economy [1]. Although organizations are already rapidly embracing it, the lack of preparation and adequate architecture can be a significant stumbling block. In fact, technology research firm Gartner predicts that half of information technology (IT) leaders will not be able to get AI projects into the production phase in 2023 [2].

That’s where AI architects come into play. This vital role could be the key to facilitating widespread AI adoption and successful AI implementation. Read on to explore the job in more detail to help determine if it’s a career path you’d like to consider. 

What is an AI architect?

AI architects lead the creation of a company's AI architecture with varying frameworks and deployment models to devise and execute AI architecture strategy. AI architects work closely with other teams, including data scientists, machine learning operations, and company leadership and stakeholders. As an AI architect, you’ll be deeply involved in helping organizations move forward with integrating AI into their existing systems and prepare their systems for new programs and applications to keep pace with emerging trends. 

To better understand AI architects, consider how it differs from similar roles. Below is an overview of how AI architect jobs differ from AI developers, AI engineers, and network architects.

Read more: Cloud Architect Career Guide: 10 In-Demand Jobs and Skills

AI architect vs. AI developer

AI architects focus on AI-related systems and the infrastructure needed to support them. Meanwhile, AI developers actively create applications and programs to meet business needs. AI developers take part in brainstorming, creation, testing, and deployment. 

Read more: AI Developer Career Guide and Roadmap

AI architect vs. AI engineer

While AI architects implement and manage AI systems and the necessary infrastructure, AI engineers build AI-based solutions to solve specific needs and challenges. They work on algorithms and must be comfortable working with machine learning, data, and coding to create and develop models, test them, and implement applications successfully.

Read more: What Is an AI Engineer? (And How to Become One)

AI architect vs. network architect

Network architects work on an organization’s broad network and its functions. AI architects focus solely on creating the architecture necessary for AI programs and applications. 

Network architects analyze businesses' networking needs and design computer networks to meet them. They may strategize to anticipate meeting future needs and often oversee system administrators, software developers, and engineers. The role is evolving to keep up with quantum computing and the speed that comes along with it.

What does an AI architect do?

In this job, you will create the necessary infrastructure to support AI initiatives, often working with a team to develop AI architecture, manage deployment, and identify uses for current and future business needs. The job involves strategizing and making decisions about AI applications and systems.

Typical AI architect tasks and responsibilities

An AI architect performs various duties to fulfill the complex role in which they work. Research from Robert Half, a professional recruiting company, suggests that six out of 10 business leaders believe AI technologies are critical to their business’s future success [3]. AI architects help ensure that organizations’ investments in the technology and its execution will be successful. 

As an AI architect, your duties will vary depending on the company's needs. Some of the primary responsibilities you can expect include:

• Developing AI models, systems, and infrastructure to help drive organizational improvements and consumer products.

• Working with other IT team members, including data scientists and leaders, to support digital transformation.

• Building systems that teams, departments, or companies can integrate into existing systems.

• Developing new AI-related applications and managing programmers.

• Implementing machine learning models and converting them into application programming interfaces (APIs) for various uses.

• Helping define AI architecture and guide leaders and decision-makers in choosing compatible technologies.

• Collaborating with security professionals to manage potential risks and implement AI technologies, applications, and infrastructure in keeping with ethical policies.

Essential AI architect skills 

As an AI architect, you'll need to understand the business's needs and have the ability to strategize various techniques to transform existing IT processes in ways that allow for AI adoption. You will need a robust background in technical and workplace skills to succeed in your role. Technical skills represent the practical aspects of your role, while workplace skills contribute to how productive you are in a work environment.

Technical skills

• Machine learning models and natural language processing

• AI infrastructure, application deployment, and operations

• Knowledge of data management and governance

• Tools like Kubernetes and Git

• Data system design and deployment

• Analytics and programming tools, including Python and R

• Knowledge in updated AI trends

Workplace skills

• Collaboration and teamwork

• Analytical and critical thinking

• Planning and organization

• Leadership and willingness to embrace change

• Ability to present findings and strategies to leaders and stakeholders

AI architect salary and job outlook

The US Bureau of Labor Statistics (BLS) anticipates strong job growth for all computer and information technology professions for the decade leading up to 2032. BLS expects database architect jobs to grow by 8 percent, while it predicts computer and information research scientist jobs will increase by 23 percent [4, 5]. Although the BLS doesn’t offer a prediction specific to AI architects, its optimism for all IT professions, combined with the rapid growth of AI, paints a positive picture for anyone working in AI. 

Additionally, the career comes with solid earning potential. According to Glassdoor, the average base salary for an AI architect in the US is $142,663 [6]. You may have opportunities to earn extra compensation with bonuses and other forms of additional pay.

AI architect career path

Now that you have a clearer idea of an AI architect's role, let’s explore what it may take to enter the field. 

Education

You may be able to get a job as an AI architect without a degree, provided you have the relevant skills. However, employers often look for candidates with at least a bachelor’s degree. Typical areas of study include data science, computer science, and programming. Having a background with training in areas like algorithms, statistics, and AI tools can be helpful, as can building your skills in working with Apache Spark and other big data systems.

Tip: A small number of universities offer AI-specific degrees. Learn more in our Guide to Artificial Intelligence (AI) Degrees.

Experience

Companies hiring AI architects are likely willing to invest in technology and professionals with the necessary expertise to guide them through adopting AI. Gaining work experience and developing robust skills can help move your career forward. Completing an internship and working in entry-level jobs, including those in development and programming, can help you build your skill set and your resume.

Read more: 6 Artificial Intelligence (AI) Jobs to Consider

Certifications

Certifications are a great way to establish credibility and provide potential employers and clients with tangible evidence of your expertise. For example, the Certified Artificial Intelligence Scientist (CAIS) credential from the United States Artificial Intelligence Institute (USAII) is aimed at leaders who want to drive business growth using AI's transformative power. You may also explore platform-specific options or certifications in particular technologies and tools. 

A few certifications to consider include the following: 

• AWS Certified Machine Learning – Specialty

• Microsoft Certified: Azure AI Fundamentals 

• USAII Certified Artificial Intelligence Engineer (CAIE)

Read more: 5 Popular Machine Learning Certifications: Your Guide

Taking the next steps with Coursera

If you're interested in a career in AI, consider taking courses to learn more about the field. You can get an excellent introduction to AI, neural networks, and machine learning with an Introduction to Generative AI offered by Google Cloud. Or, develop your coding skills and learn about the scope of AI with the IBM Applied AI Professional Certificate. You’ll find many courses and programs designed to help you discover more about the field or become career-ready on Coursera. Upon completing either program, gain a shareable Professional Certificate to include in your resume, CV, or LinkedIn profile.