10 Software Engineering Skills Needed to Lead in the AI Economy

It has been predicted that AI will contribute $19.9 trillion to the global economy through 2030. However, if this potential is to be realized, there will be an acute need for technically skilled leaders and software engineering professionals able to bridge the gap between the remarkable emerging capabilities of foundation AI models such as ChatGPT, Claude, and Gemini, and the specific needs of individual companies, industries, and individuals. Currently, there is an acute shortage of professionals and leaders with the necessary skills for this work, which will be required in every business, large and small.

While many powerful foundation models have already become available, the wave of AI adoption that will occur within all businesses throughout the economy over the coming few years to a decade is still in its infancy. Those with the key skills to enable this rollout at this time stand to ride an incredible wave of opportunity and innovation.

Located squarely in the center of this space is Quantic’s MS Software Engineering program, which is uniquely well-placed to provide the Masters-level training needed to prepare candidates, within the timeframe of one year, with the skills needed to help lead in and contribute to this economic revolution.

What Software Engineering Professionals Will Play a Key Role in Realizing the AI Economy?

The Growing Demand for Skilled Software Engineers in the AI Economy

While foundation models have incredible and rapidly increasing capabilities, they do not provide fully customized and reliable solutions for each business use case throughout the economy. This can be because the foundation models do not (and cannot) have access to the businesses’ internal proprietary data resources, do not understand the subtleties or the norms of the interactions required in the given organization or industry or cannot reliably carry out extended activities involved in typical workflows in the organization, amongst a range of other subtle and complex real-world challenges.

Here it becomes essential that skilled software engineering professionals are available who are able to lead the development of initiatives and the building of software systems that harness these powerful large language models (LLMs) to reliably implement the specific needs of individual businesses and industries. What is needed is a sophisticated range of skills from AI strategy, aspects of full-stack engineering, LLM calling, prompt engineering, orchestration and fine-tuning, the use of cloud services, cloud hosting for model inference, and a strong knowledge of modern software architectures and containerization. These are many of the key skills required for an AI Engineering role, all of which are covered in Quantic’s MS Software Engineering degree.

Entrepreneurial Opportunities for Software Engineers in AI

While foundation models have incredible and rapidly increasing capabilities, they do not provide fully customized and reliable solutions for each business use case throughout the economy. This can be because the foundation models do not (and cannot) have access to the businesses’ internal proprietary data resources, do not understand the subtleties or the norms of the interactions required in the given organization or industry, or cannot reliably carry out extended activities involved in typical workflows in the organization, amongst a range of other subtle and complex real-world challenges.

Here, it becomes essential that skilled software engineering professionals are available to lead the development of initiatives and the building of software systems that harness these powerful large language models (LLMs) to reliably implement the specific needs of individual businesses and industries. What is needed is a sophisticated range of skills from AI strategy, aspects of full-stack engineering, LLM calling, prompt engineering, orchestration and fine-tuning, the use of cloud services, cloud hosting for model inference, and a strong knowledge of modern software architectures and containerization. These are many of the key skills required for an AI Engineering role, all of which are covered in Quantic’s MS Software Engineering degree.

10 Must-Have Software Engineering Skills to Lead in the AI Economy

A Deep Understanding of Current AI Capabilities

Understanding what can and cannot be implemented with current AI model capabilities, as well as how feasible or alternatively hard a given use case is to implement, depends on a deep understanding of current AI capabilities. For example, recent developments in voice-based AI model application programming interfaces (APIs) make a whole range of innovative voice-based AI applications now possible that, just a few months ago, were not possible.

AI Strategy and Project Management

Being able to plan and architect the approach needed to manage the implementation of a given AI initiative will be key. This will include an understanding of what technologies to utilize, how to do this cost-effectively, and how to manage the project work in an agile manner. For example, some AI initiatives can be achieved through prompt engineering alone, while others may require complex application development.

AI-Driven Software Engineering Skills

Current developments in AI-based code generation enable the individual software engineer to be increasingly productive. Being an AI-first developer who utilizes these tools to dramatically increase productivity in many aspects of system development will be key to being maximally effective and productive going forward. For example, the use of AI-powered integrated development environments (IDEs) such as Cursor, Windsurf, Replit or various VSCode extensions are rapidly becoming the norm in software development.

Leadership and Organizational Strategy for AI Projects

In leading software engineering and AI initiatives, expertise in leadership and organizational strategy is required. This involves understanding the fundamentals of management, how to manage talent, how to work in teams and groups, and how to provide positive leadership. This is also combined with agile project management practices. For example, to build, maintain, and motivate a high-functioning team, all of these skills are needed.

Mastery of Cloud-Native Applications and Architectures

Modern software architectures rely heavily on cloud services, so a deep understanding of cloud architectures is central to modern software engineering. Additionally, access to cloud-based GPU hardware offers tremendous advantages for the deployment of AI models in terms of capital expenditure savings and also elastic scalability based on need or load.

AI Engineering Skills for Real-World Applications

A range of novel skills and technologies are required for AI Engineering. These relate to topics such as the use and integration of vector stores, retrieval augmented generation, the management of model context, the use of AI orchestration frameworks, and prompt engineering. An understanding of these, combined with a knowledge of the rapid evolution of vendor offerings related to these capabilities, is essential in the field. For example, if your organization has internal proprietary data, skills in these areas will support the integration of these resources with the intelligence provided by AI models. 

Machine Learning Skills

Historically, machine learning skills are needed for the training and development of custom predictive models. Modern AI models can significantly decrease the need for the training of custom models. However, these skills continue to have an important role in models optimized for structured data. Additionally, machine learning skills are needed to fine-tune modern AI models. For example, a foundation model may not respond according to the particular style and parlance of your business use case – in such cases, fine-tuning an AI model can provide an appropriate solution, and fine-tuning borrows many concepts from traditional machine learning.

Continuous Integration, Testing, and Deployment for AI Systems

Continuous integration/continuous deployment, or CI/CD as it is commonly referred to, is an approach that provides the ability to rapidly and iteratively deploy incrementally improving software systems. These techniques are also key to the deployment of modern LLM-based systems. For example, these techniques allow end-user feedback to be received and addressed, and an updated application addressing critical feedback, where received, can be redeployed within the day.

Understanding of Modern Software Architectures

For enterprise-scale AI applications, understanding the application of various modern software architectures is important. Skills needed in this area include domain-driven design, microservice architectures, and event-driven architectures, amongst numerous other patterns. By utilizing the correct architecture for the given problem, scalability, cost, and reliability can all be improved. 

Communication Skills

Being able to bridge the technical and business terminology gap is key to working and communicating in this field. This involves understanding the business domain and business terminology, in addition to having comprehensive knowledge of AI and software concepts and terminology.

All of these skills, and others, are covered and developed within Quantic’s MS Software Engineering program.

Top Career Opportunities in Software Engineering for the AI Economy

AI Engineer

AI Engineers design, develop, and deploy AI-powered systems using large language models, machine learning, and advanced algorithms. They play a critical role in creating scalable solutions tailored to business-specific needs in the AI economy.

Director of AI

Directors of AI lead strategic AI initiatives, overseeing the integration of machine learning models and AI tools to meet organizational goals. They manage teams and ensure AI technologies drive innovation and business growth across industries.

Vice President of AI

The Vice President of AI shapes company-wide AI strategies, aligning AI innovations with long-term business visions. They lead cross-functional teams, manage budgets, and ensure cutting-edge AI solutions deliver measurable economic and operational value.

AI-enabled Software Engineer

AI-enabled software engineers combine traditional coding with AI technologies to create intelligent applications. They utilize tools like AI-driven IDEs and prompt engineering to build smarter, more efficient software systems for modern organizations.

DevOps Engineer with an AI Focus

DevOps Engineers with an AI focus streamline AI model deployment through CI/CD pipelines, cloud-native architectures, and containerization. They ensure seamless integration of AI systems into production environments while maintaining scalability and performance.

Quantic’s MSSE program offers career support, networking opportunities, and an alumni network to help graduates secure these roles.

Why Choose Quantic’s MSSE for Aspiring AI Leaders

Flexible, Mobile-First Learning: Quantic’s award-winning, interactive platform allows students to learn anytime, anywhere, with the flexibility to balance their studies alongside professional and personal commitments. Integrated Curriculum

AI-Integrated Curriculum: Gain cutting-edge knowledge in AI-augmented software engineering, the engineering of AI systems, and AI strategy and project leadership.

Career-Boosting Connections: Students connect and collaborate with a global cohort of peers, industry mentors, and faculty. The program includes virtual academic events, optional in-person meetups in cities around the world, and access to student-led organizations, providing unparalleled opportunities for professional growth and networking.

Affordable and Accessible Education: At just $950 per month for the 13-month program, Quantic’s MSSE offers exceptional value. Students often take advantage of employer reimbursements, tuition benefits, and scholarships to reduce costs further, making the program accessible to a wide range of professionals.

Embrace the Opportunities of the Emerging AI Economy

While AI promises to transform the global economy, the rollout of AI into each existing business is still in its infancy, as is the formation of the numerous new AI-first businesses that will emerge. Having advanced AI technical skills is essential for contributing to realizing economic growth nationally, but it also provides great career opportunities for each graduate individually to participate in and help lead in this AI revolution.

Ready to take the next step? Explore Quantic’s MSSE program to gain the skills and knowledge required to lead in the AI Economy. You are encouraged to reach out to Quantic with any questions you may have.

Discover why our students love us and why we’re trusted by top employers around the world by reading Quantic reviews here.

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