Like every other profession in the world, artificial intelligence is fast redefining software engineering roles in Australia. Routine tasks like coding, testing and deployment are increasingly automated. The skills that set candidates apart are, as a result, changing.   

As Launch Senior Recruitment Consultant, Imogen Hough , puts it, “AI is being used for prompts, code generation, debugging and deployment. We are seeing companies look for people who are strong in system design, design patterns, architecture and problem solving, not just coding.”   

Naturally, these developments alter the expectations for software engineering roles, especially for junior engineers, pushing everyone to think more strategically about systems, design and architecture.  

In the last year, especially, AI has become an almost universally standard tool in the developer workflow. Platforms like GitHub Copilot and Amazon CodeWhisperer are already widely adopted, with surveys showing more than 60% of software engineers in Australia use AI assistants in some part of their coding process.   

As Imogen points out, “The engineers that are upskilling themselves within AI and machine learning are the ones that are having a competitive edge in the market.”  

The impact of AI adoption is twofold. On one side, companies can accelerate delivery by offloading repetitive tasks. On the other, engineers are expected to step into more complex work, requiring greater judgment and foresight. This is the shift leading employers to give greater weight to problem-solving ability, system design and architecture skills when they hire.  

While AI takes care of the repetitive elements of coding, the work that’s left is the tasks that cannot (yet) be automated. Robust system design, scalable architecture and an understanding of design patterns still require a high level of human touch and have become the markers of seniority and capability. These skills ensure that software remains maintainable, secure and efficient even as development speeds up with AI tools.  

Imogen explains, “Clients aren’t asking for a ‘typical’ developer anymore. They want the top-tier talent with experience in system design, design patterns and architecture. Having that broad understanding is what is going to make people really stand out.”  

This expectation isn’t limited to the big tech firms. Australian banks, consultancies, and start-ups are all asking the same questions. They want candidates to demonstrate how they approach architecture and complex problem-solving, not just which languages they can code in. 

It’s clear that senior engineers in Australia will need to show breadth and depth across their tech stacks and their ability to design resilient systems that last. But what about junior engineers who haven’t yet developed that experience?   

This deviation in expectations presents a big challenge for graduates and early-career developers. Most juniors cut their teeth in entry-level roles, focusing on bug fixes or basic testing, the tasks that are now partly absorbed by AI.   

“It’s going to be a slightly tougher market for juniors,” says Imogen, “the simpler tasks are now being covered by AI.”  

That doesn’t mean juniors have no path forward. It just means the bar has moved. Rather than looking for a degree and coding basics, they will be looking for evidence of AI capability and a strategic systems mindset. Imogen advises junior candidates to:   

• Build AI literacy early : understand how tools like Copilot and CodeWhisperer work, even at a basic level.  

• Seek exposure to design concepts : learn the principles of clean design and architecture, not just syntax.  

• Network with intent : industry connections and referrals often open doors that job boards alone cannot.  

• Customise every CV : show alignment with the role and organisation instead of submitting generic applications.  

For early-career engineers, standing out will increasingly come down to showing potential: the willingness to learn, the ability to collaborate, and evidence of initiative outside of formal coursework.

For both juniors and experienced engineers, the consistent theme is the need to keep learning. The market is rewarding those who extend their skills beyond core coding into AI, ML and systems thinking.  

Practical ways to build that edge include:  

• Take structured courses : explore Coursera , Udemy and edX modules in machine learning, generative AI and LLMs.  

• Practise system design : use resources like Grokking the System Design Interview or contribute to open-source architecture projects.  

• Experiment with AI tools : go beyond code generation to understand where tools fit into workflows.  

• Showcase initiative : maintain a GitHub profile or blog with recent projects that highlight curiosity and growth.  

• Stay language-agnostic : being open to multiple stacks signals adaptability and readiness for enterprise-scale roles.  

“If you’re active on GitHub or a blog, you already have a competitive edge before the interview stage,” advises Imogen. “Clients are then not just looking at a piece of paper with your listed skills, they’re actively looking at your code.”  

Learning the right skills is one part of the picture. To be considered seriously, the other has to be showing employers clear evidence of those skills before and during the interview process.  

• Make your application specific : employers expect a CV that reflects their role and organisation, not something generic.  

• Provide evidence upfront : link to GitHub, a blog, or recent projects so hiring managers can see your work before meeting you.  

• Show progression : highlight how you’ve added new tools, stacks or approaches over time, signalling adaptability.   

• Explain your thinking : be ready to talk through design or architecture choices and the trade-offs you made.  

• Connect skills to outcomes : describe how your work improved scalability, performance, or resilience.  

• Demonstrate adaptability : share a story of how you learned a new language, framework, or AI tool quickly and applied it effectively.  

The key to standing out is to effectively show how you can work alongside AI while adding the human judgment, creativity and design skills that machines cannot replicate.  

There may be a lot of rapid change, with a lot more sure to come, but there is no reason to believe that AI is replacing demand for software engineering roles in Australia.   

The automation of routine work is just shifting the spotlight to the skills that only people can bring: system thinking, architectural design, problem-solving and adaptability.  

For senior engineers, that means demonstrating breadth and depth across stacks and the ability to design resilient systems that scale. For juniors, it means finding ways to show potential through AI literacy, curiosity and initiative, even without years of experience.  

As Imogen emphasises, those who invest in upskilling and show how they apply their knowledge are the ones gaining the edge. The engineers who will thrive are those who can integrate AI into their workflow while proving the creativity and judgment that no algorithm can replace.  

To learn more about AI’s impact on Australia’s software engineering market, contact Imogen in Sydney or our Melbourne software engineering consultant, Aimee Thompson.