Beyond the code: Preparing software engineers for the AI era

With generative artificial intelligence (AI) tools now capable of writing code, building apps and automating routine tasks, the traditional pathway into software engineering is evolving. One question looms large for students and early-career professionals:

Where are the entry-level developer jobs going?

According to Dr. Qusay H. Mahmoud, Professor and Assistant Dean, Engineering Outreach in Ontario Tech University’s Faculty of Engineering and Applied Science, this transformation opens the door to a new engineering mindset rooted in systems thinking, ethical design and human-AI collaboration, and entrepreneurial creativity.  

Dr. Mahmoud shares his perspective on how software engineering students can future-proof their careers and thrive in a world where AI is not a threat, but a useful tool:

What tasks traditionally done by junior developers can now be automated by AI tools? How is this affecting entry-level developer hiring practices?

Tools like GitHub Copilot and other AI coding assistants can now partially automate, and accelerate many routine tasks from repetitive coding and basic interface design to testing, code translation and documentation.

Here’s how it’s affecting hiring for junior roles:

• The traditional junior role is shrinking: Jobs focused mainly on routine bug fixes or isolated coding tasks are becoming less common as companies seek early-career engineers with broader systems awareness, not only the ability to write correct code.
• The bar for entry-level developers is moving up: Employers want people who can reason about a system, spot when the AI is wrong, and diagnose and fix more complex failures.
• AI fluency is becoming essential: Junior developers are expected to prompt effectively, evaluate outputs critically, and integrate AI-generated code responsibly.

What does this mean for students considering a career in software engineering? Where will humans continue to add value as AI advances?

Students should update their mental picture of the field. The real value lies not in producing code but in shaping, integrating and taking responsibility for intelligent systems.

AI can generate code, but it can’t take responsibility for a system. We still need humans to interpret real requirements, validate assumptions, make architectural and security decisions, and ensure the systems we build are safe, reliable and ethically grounded. Those responsibilities define the profession, and they can’t be delegated to AI.

My advice to students is simple: learn to collaborate with AI in ways that make you better at the parts of engineering that matter most. The future engineer is the person who can guide, evaluate and correct AI systems while making thoughtful design and security decisions. That’s where the meaningful work and the long-term opportunity will be.

What skills should aspiring developers focus on to stay competitive?

Aspiring developers should focus on four skill areas that will remain important even as AI becomes more capable:

• Strong fundamentals: AI can generate code, but it can’t tell you whether a design or algorithm will work under real constraints. A solid grounding in data structures, algorithms, operating systems, networking, databases, and core software engineering principles like modularity, abstraction, cohesion, secure-by-design thinking, and testing will let you reason about any system.
• Systems and architecture: Understand how software systems are designed to communicate, respond to user actions, and scale across multiple computers, as well as how modern applications are often broken into small independent microservices to improve flexibility. You should also know how to identify security risks, plan how to prevent them, and think about performance, scalability and reliability. It’s the difference between saying ‘I built a feature’ and ‘I helped design a system’.
• AI-aware development: AI often produces code that looks correct but is subtly wrong or insecure. You need to know how to give AI tools clear instructions, refine the results through back-and-forth adjustments, and check that the final output is accurate and reliable.
• Human skills: Can you explain technical choices to non-technical project collaborators? Write a clear design document? Participate productively in code reviews? As systems grow more complex and AI-assisted, aligning people around a shared understanding through effective communication and collaboration becomes a real superpower.

How can students gain real-world experience that leads to higher-level roles if entry-level jobs are shrinking?

Students can demonstrate credibility and genuine responsibility through co-op work terms, side projects, open-source contributions, undergraduate research, or even on-campus technical roles such as supporting a research lab or building student club software. These experiences expose students to real constraints, design decisions and accountability, which is what employers value.

AI makes it easier to build ambitious projects or even prototype entrepreneurial ideas, but only if you truly understand and take ownership of what you’re creating. What sets you apart is your ability to explain the system, defend its architecture, and fix it when something breaks. That depth is what prepares you for higher-level roles.

How does Ontario Tech’s Software Engineering program prepare students for this shift?

Ontario Tech’s accredited Software Engineering program continuously evolves to help students master the parts of engineering that still require human judgement and that AI can’t replace.

Students build strong math and engineering foundations, progress from programming into full systems design and architecture, and learn software engineering as a life cycle that includes quality, security, ethics and long-term maintenance. They also develop practical skills in machine learning and data-driven systems, preparing them to work with the AI-enabled technologies increasingly embedded in modern engineering practice. Our curriculum is hands-on and project-focused, and the Software Engineering program (like all of our Engineering programs) offers a co-op stream that integrates academic study with paid, career-connected work experience.

We also integrate AI-assisted development into assignments and projects so students learn how to interpret, evaluate and correct AI-generated code. That ability to work above AI, rather than compete with it, is exactly the skill industry now expects from the next generation of engineers.

What’s the long-term outlook for software engineering careers in an AI-driven world?

I’m optimistic about the long-term outlook. AI will automate more repetitive coding work, while the world becomes more dependent on software, and increasingly AI-enabled. This raises the complexity of the systems we build, increasing the need for skilled engineers. Fully autonomous software engineering remains a research challenge, especially for secure systems design, major changes to complex existing software, legacy integration, and real-world operation, which is why the work is shifting, not disappearing.

We’ll see more roles centred on architecture, integration, security, reliability, and applying expertise to real-world problems in specific fields. Individual engineers will become more productive, and their value will rise as they learn to use AI as an amplifier rather than a crutch. This is where human judgment, communication and systems thinking remain essential.

What advice would you give to someone worried about being replaced by AI?

AI replaces tasks, not people. It’s better to ask yourself, ‘How can I use AI to extend what I’m capable of?’

Whether you’re in engineering, business, health care, education or the arts, the people who thrive will be the ones who learn how to work with AI: understanding when to trust it, when to question it, and how to bring human judgment, empathy, creativity and ethical reasoning to the table. If you stay curious, keep building real skills, and use AI as a tool rather than seeing it as a threat, you’ll put yourself in a position where AI amplifies your value instead of competing with you. The future belongs to people who can direct and interpret AI, not just consume its output, and that is something everyone can learn.