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I've been placing software engineers for 21 years. I've lived through the cloud boom, the DevOps explosion, the great TypeScript migration. I thought I'd seen every iteration of "we need someone who can do X but we have no idea what X actually means." Then came the AI engineer. First, Let's Get the Title Right Before I talk about the market, I need to address something that causes genuine confusion, for clients, for candidates, and honestly sometimes for me. When a hiring manager tells me they need an "AI engineer," they almost certainly don't mean someone who trains large language models from scratch or has a PhD in machine learning. That's a different discipline entirely- closer to research science than product engineering. The role that's suddenly in enormous demand is something different: a software engineer who can build production systems that use AI as a component. Think of it this way. An ML engineer builds the engine. An AI engineer builds the car. In practice, this means: integrating LLM APIs (OpenAI, Anthropic, Google Gemini) into applications; building retrieval-augmented generation (RAG) pipelines; working with vector databases; designing agentic workflows; evaluating and monitoring AI outputs in production. These are fundamentally software engineering problems: system design, reliability, latency, cost, security, with a layer of AI-specific thinking on top. The industry hasn't fully settled on a consistent job title yet. You'll see the same role advertised as "AI Engineer," "Applied AI Engineer," "LLM Engineer," or just "Senior Software Engineer (AI focus)." The ambiguity is real, and it's one of the first things I have to untangle with every new client brief. The Australian Market: Hot Demand, Thin Supply The numbers are hard to ignore. AI Engineer is now the fastest-growing role in Australia. References to AI in Australian job postings on Indeed more than doubled in a single year, from 3.3% to 6.2% of all postings between early 2025 and early 2026, with software development roles at the sharp end of that shift. Roles with genuine AI fluency are commanding salary premiums of 20–30% over comparable non-AI engineering positions. My clients are feeling this. Across the boards I work with: fintechs, scaleups, enterprise software companies, there is a consistent story: they know they need to ship AI features, they have budget, and they cannot find engineers who can actually do it. Here's the problem I keep running into: there hasn't been enough time for the commercial talent pool to develop. The generative AI wave that changed everything, the one that made RAG pipelines and LLM integration a core engineering skill, is, in real terms, only about two years old in terms of production deployment. ChatGPT launched publicly in late 2022. The tooling matured through 2023. The first wave of serious commercial AI products in Australia started shipping in 2024. That's not a long runway. Compare this to cloud engineering, where engineers had close to a decade of AWS/Azure production experience before "cloud engineer" became a standard hiring category. AI engineering doesn't have that history yet. The engineers who genuinely know how to build and ship robust AI systems in production, not just prototype a chatbot, but properly architect it, evaluate it, handle failure modes, manage cost and latency at scale, are rare because the opportunities to gain that experience commercially have only recently existed. This is compounded by the fact that Australia's enterprise AI adoption has been cautious. A 2024 Deloitte survey found 49% of Australian business leaders cited skills shortage as the biggest barrier to AI adoption. 14 percentage points above the global average. Two-thirds of Australian SMBs now say they use AI, but only around 5% report genuinely capturing meaningful business value from it. A lot of what's been happening is experimentation and proof-of-concept work, not production systems at scale. That means fewer engineers have been able to rack up the kind of real-world reps that make a strong CV. The result: a very small pool of candidates who have done this properly, significant competition for every credible one of them, and a large pool of engineers who are trying to break in but haven't had the opportunity yet. What I See Clients Getting Wrong A few recurring mistakes on the hiring side that I'll flag here, diplomatically: Conflating AI engineers with data scientists or ML engineers. These are related disciplines, not interchangeable ones. If you're building a product feature powered by an existing LLM API, you need strong software engineering skills plus AI-specific knowledge. You probably don't need someone who can train neural networks from scratch. Writing job descriptions that are unrealistic. I regularly see briefs asking for "5+ years of AI engineering experience" in a field that barely existed three years ago in commercial form. This immediately signals to candidates that the hiring team doesn't understand the space - which is its own problem for attraction. Undervaluing strong software engineers who are AI-curious. Some of the best hires I've made in this space have been excellent engineers with 7–10 years of backend or full-stack experience who have been actively learning AI engineering in the past year. They may not have two years of production LLM work on their CV, but they know how to build real systems- and that foundation matters enormously. Overindexing on the AI part, undervaluing the engineering part. I've seen candidates with impressive prompt engineering skills who struggle with the actual systems work - API design, observability, handling failures gracefully, thinking about scale and cost. The engineering fundamentals still matter, perhaps more than ever. Advice for Software Engineers Who Want to Move into AI Engineering If you're a software engineer reading this, and you're wondering how to position yourself in this market; here's my honest read on what will actually move the needle for you. Build something real, and deploy it. Side projects matter here more than almost any other discipline I recruit in, because commercial experience is scarce. A RAG-powered app, an AI agent that does something genuinely useful, an internal tool you built for your current employer. Anything that demonstrates you've grappled with the actual engineering problems: chunking strategies, retrieval quality, prompt design, evaluation, cost management, latency. Running a notebook locally doesn't count. Shipping something does. Get serious about the core infrastructure skills. The AI engineering stack is not just "call an LLM API and return the response." The engineers who stand out know how to work with vector databases (Pinecone, Weaviate, Chroma, pgvector), understand embedding models and retrieval mechanics, can design and evaluate a RAG pipeline, and know how to use orchestration frameworks like LangChain or LlamaIndex without treating them as black boxes. These are learnable skills, and there are good resources to learn them, but you need to have actually used them. Understand evaluation. Seriously. One of the most under-appreciated skills in AI engineering is knowing how to measure whether your system is working. LLMs don't fail like traditional software. They degrade gradually, hallucinate plausibly, and behave differently across context windows. Engineers who know how to design evaluation frameworks, set up observability, and catch regressions are genuinely rare. This is an area where even experienced engineers often have gaps. Know your way around at least one major cloud AI ecosystem. AWS Bedrock, Azure OpenAI, Google Vertex A - your clients will likely be deployed on one of these, and knowing how the managed AI services integrate with the surrounding cloud infrastructure (IAM, VPCs, logging, cost controls) is commercially valuable. Pure API knowledge isn't enough at scale. Don't neglect the software engineering fundamentals. This sounds obvious, but it's worth saying: the engineers who succeed in this space are good engineers first. System design, API design, testing discipline, observability, performance thinking. These skills matter as much in AI systems as in any other distributed system, arguably more, because the failure modes are less predictable. Start contributing to the conversation publicly. In a market where most hiring managers can't fully assess AI engineering skills themselves, visible work carries outsized weight. Write about what you've built. Contribute to open-source projects in the AI tooling space. Speak at a local meetup. Not as a marketing exercise but as a forcing function to actually think deeply about what you're doing and why. The engineers who are landing the best roles right now are often the ones who've been visible in the community. Consider the adjacent roles as a stepping stone. If you're a backend engineer at a company that's starting to build AI features, raise your hand to be involved, even if it's not your primary responsibility. Contributing to an AI feature at your current employer is a far faster path to credible experience than studying alone. The commercial context: the real constraints of production systems, business requirements, reliability expectations, is where the genuine learning happens. Where This Market Is Heading The demand isn't slowing down. The Tech Council of Australia projects around 200,000 AI-related jobs in Australia by 2030. The shift from AI experimentation to AI in production (which is the phase we're entering now, with over half of Australian organisations expecting to have AI experiments in production by mid-2026) means the appetite for engineers who can actually ship this stuff will only grow. The supply shortage will ease over the next few years as more engineers accumulate real experience and training programs catch up. But right now, the gap between what clients need and what the market can supply is real, and it's significant. For software engineers, the window to establish yourself in this discipline, before it becomes as competitive as cloud or DevOps, is open. But it's not infinitely open. The engineers who are building genuine skills and shipping real things now will be well placed when the next wave of hiring accelerates. For my clients: the talent is out there, but you may need to think differently about what the profile looks like. The best AI engineers aren't always the ones with the most AI on their CV, sometimes they're excellent software engineers who've been investing in this seriously and just need the opportunity to prove it in production. That's what I'm here to help you find. I am a software engineering/ AI engineering recruiter based in Melbourne, Australia, specialising in placing engineers across product, platform, and AI engineering roles. If you're hiring or looking, get in touch! Lindie.Graham@pra.com.au

Somewhere in the world right now, a hiring manager is asking a question… and three seconds later ChatGPT is answering it.

The AI Authenticity Gap: Why Your AI-Generated CV Might Be Costing You the Job I see hundreds of CVs every week. I spend more time on LinkedIn than I care to admit. And one thing that's becoming increasingly prevalent is the appearance of overly authored posts and descriptions with plenty of words but precious little substance. Much of this has coincided with the widespread adoption of tools like ChatGPT. As someone working adjacent to the tech space, I was genuinely excited when AI started making waves across the world. I thought this was going to be a real game changer, and in many ways, it has been. But the overuse of generic AI-generated content has become so prevalent that I feel some people are now failing to show their authentic voice - the very thing that makes them stand out in a competitive market. The Early Adopter's Reality Check I was one of those people who tried to adapt early to AI, using it to help me in my professional and personal life. But here's the crucial difference: I didn't just accept the standard output I was given. I took the bones and made them my own. I used AI as a tool, not as a ghost-writer. Too often now, I see CVs that have been completely assembled by ChatGPT - so generic, so obviously automated, that I genuinely feel the candidate would have been better off not sending anything at all. These applications don't just blend into the background; they actively work against the candidate by signalling a lack of effort and authenticity. The Numbers Don't Lie Recent research validates what recruiters like myself are seeing daily. A May 2025 survey of 600 U.S. hiring managers revealed some startling statistics: One in five recruiters (19.6%) would outright reject a candidate with an AI-generated resume or cover letter Over a third of hiring managers (33.5%) can spot an AI-generated resume in under 20 seconds 58% of hiring managers express concern about AI-generated applications Think about that for a moment. Hiring managers are detecting AI-written CVs in less time than it takes to read a single paragraph. The very tool candidates think gives them an edge is often the red flag that gets them filtered out. The Efficiency Versus Laziness Debate When ChatGPT first emerged, many of my colleagues said outright that this was going to make people lazy. I argued against that view. I believed that just as Excel made formulating reports easier without making us worse at analysis, ChatGPT would help people be more efficient in their work - freeing them up to focus on strategic thinking and creative problem-solving rather than getting bogged down in formatting and structure. I still believe AI can be a powerful efficiency tool when used correctly. The problem is that many candidates aren't using it to enhance their work; they're using it to replace their work entirely. The Personal Touch in an AI World While improvements are being made to make AI-generated content seem less generic, there's a fundamental issue when you're putting forward something meant to be a representation of yourself. Your CV is your professional story. It's your opportunity to showcase not just what you've done, but who you are, how you think, and what makes you different from the hundreds of other applicants. When you rely on AI to put it all together, you lose all control and that crucial personal touch. The research backs this up: Baby Boomers and Gen X hiring managers are particularly sceptical, with one in four Baby Boomer managers likely to reject fully AI-generated resumes. Even among younger Millennials and Gen Z managers, who you might expect to be more accepting of AI use, there's a clear expectation that the final product must sound human, show real effort, and reflect the individual behind the words. The Right Way to Use AI in Your Job Search By all means, use the tools available to you. AI can be excellent for: Brainstorming bullet points you might have forgotten Identifying gaps in your experience narrative Improving grammar and clarity in your existing writing Suggesting different ways to frame an achievement Creating a first draft structure that you then completely personalise But don't think that because you can do something quickly and easily, you're going to get the same results as someone who actually takes the time to show they've invested effort. The data shows that 74% of hiring managers have encountered AI-generated content in applications, and they're becoming increasingly adept at spotting it. Standing Out in a Tough Market It's a challenging market out there in many sectors of the technology industry. If you want to stand out from the crowd, you need to ensure you can show exactly who you are. That means: Write in your own voice - Not the corporate-speak that AI defaults to Include specific examples - Generic achievements sound hollow Show your personality - What drives you? What excites you about your work? Customize for each role - AI-generated applications often feel one-size-fits-all Proofread beyond grammar - Does this sound like something you would actually say? The Bottom Line The irony is that in trying to use AI to save time and improve their chances, many candidates are actually undermining themselves. They're creating a sea of sameness in which their application drowns rather than floats to the top. Remember: hiring managers want to hire people, not algorithms. They want to understand your unique perspective, your problem-solving approach, your communication style. They want to see evidence that you've put thought and effort into your application because that's a strong indicator of the thought and effort you'll put into the job itself. Use AI as a tool in your toolkit - but make sure the final product is unmistakably, authentically you. That's what will make you stand out in 2025 and beyond. Need help crafting a CV that showcases your authentic voice while still being competitive in today's market? Get in touch, I'd be happy to provide guidance on how to strike that perfect balance between efficiency and authenticity. Article written by: Jack Davies PRA Brisbane Associate Consultant - Development and Testing M: 0483 969 454 E: jack.davies@pra.com.au

Job hunting can be tricky, but we’ve got you covered. Our 2025 PRA Job Seeker Handbook is full of tips and insights to help you: Make your applications stand out Nail your interviews Navigate offers with confidence And land the role that’s right for you Download your free copy today!

“I’ve been applying for jobs for months, and I’ve had lots of interviews, but I keep coming second, or they cancel the role, or put it on hold, and I’m starting to get worried about how I’m going to pay my bills”. I have had this conversation, or one that sounds just like it, with lots of candidates in the past few months, and it’s agonizing for them and weighs heavily on me. The part of my job I love the most is helping people put their best foot forward, highlight their skills that really matter, and hone in on what they love doing most; and help them find the role that fits that. So - aside from a tough economy and a tighter job market, what are some of the common challenges I’m seeing candidates who are job hunting have? 1) They’ve been in a job with a weird job title, or a weird mix of responsibilities/ delegations, and now they aren’t sure where they fit- which makes applying for roles and selling themselves feel hard 2) They’ve been in really senior roles, that aren’t as abundant as, well, less senior roles, and so with more competition in the market, they don’t know what to do if they can’t do the type of work they’ve been doing to date (or earn the salary they’re used to) 3) The expectations of the job they used to do have changed (due to things like AI), and so now an introverted and shy developer doesn’t have the same value proposition as a ‘consultant’ type, who can be an “analyst/programmer” or future consultant/prompt engineer. But while the job market is tough, it’s not without opportunity, and more importantly, you’re not alone in navigating it. We’re here to help guide you through it with expert advice, encouragement, and practical tools. A couple of general job-hunting ‘first impressions count’ considerations, presentation and communication: Presentation: During Covid, when everyone was working from home, for some, professional presentation standards slipped a bit — and now the market is correcting. Unfortunately, most companies do judge a book by its cover. The Halo Effect is a well-known psychology concept. If you look well-groomed and put together, people subconsciously extend that positive impression to other qualities — like competence, reliability, and honesty. This is a cognitive shortcut our brains take when forming quick judgments. Additionally, Risk Assessment Bias is a bias that means humans are wired to make rapid judgments about safety and trustworthiness — and appearance is one of the first cues we use. If someone looks orderly and conventional, it’s often interpreted as “low risk” compared to someone whose presentation is far outside the norm. Communication: Communication is universally recognised as one of the most important requirements in a new hire. In 2025, business insider highlighted that communication was the most in-demand skills even in technology where AI is advancing. While a 2025 report reveals that 92% of hiring professionals now say soft skills—including communication—are just as important as technical skills. Can you confidently say that communication is your strongest skill? Have you worked with a career coach, vocal coach or mentor to talk about how you might improve your communication skills and/ or interview skills? Recruiters are an invaluable resource to ask to provide feedback on how you interview, and if your communication skills could be improved in any way. ChatGPT can also help with suggesting and refining answers to interview questions so you can practice before a face-to-face interview. Now that piece is sorted, let’s talk about strategies you can leverage to move forward. To begin, I’ll address my first point listed at the start of this article: 1) You’ve been in a job with a weird job title, or a weird mix of responsibilities/ delegations, and now you aren’t sure where you fit. This is a great time to chat to a recruiter or a career coach (who specialises in your industry), and ask them where they think you fit, or what kind of job titles you should be looking at on job boards. An exercise I often ask candidate’s I’m working with to complete is to go on Seek and look Australia wide at jobs that are in the general ballpark of what they do, and start reading the responsibilities of these roles- note the job titles being used, and see if the job titles and responsibilities align with what they’ve been doing, and update their CV to include these key terms of titles (only if they are applicable of course!). Sometimes what you’ve been doing vs. how it’s described in other companies isn’t the same, so make sure you’ve got common language between your resume and the job ads on job boards like Seek and LinkedIn. Sometimes it’s also appropriate to have more than one CV- perhaps your most recent role was a blend of both program manager and General Manager and one CV highlight both of these things can send mixed messages, and it may be worth breaking down the responsibilities for each into two separate documents, depending on the role you’re applying for, so you don’t spook anyone that you’re “too senior” OR “too junior”! 2) You’ve been in senior roles, that aren’t as abundant as, well, less senior roles, and so with more competition in the market, you don’t know what to do if you can’t do the type of work they’ve been doing to date (or achieve the salary you’re used to). The reality of being a senior professional is that senior roles aren’t as abundant as junior roles- so this may mean looking at interim roles in the meantime, that you are qualified to do, and can still enjoy, while you wait for the perfect opportunity to arise. If you are a Director of Engineering, this might look like considering Scrum Master or Iteration Manager roles (on a contract ideally, so you’re not leaving people in the lurch when your dream role comes along in the medium term). If you’re an Operations Manager, perhaps looking a process improvement roles or other coordination type roles (depending on the nature of your role) could be an ideal stop gap. Once again, it may be worth having two CV’s that highlight the respective skills you have at a more senior level and a less senior level. 3) The expectations of the job they used to do have changed (due to things like AI), and so now an introverted and shy developer doesn’t have the same value proposition as a ‘consultant’ type, who can be an “analyst/programmer” or future consultant/prompt engineer. The key here is to lean into your strengths while building complementary skills that boost your value proposition. Let’s be clear- you don’t need to suddenly become an extrovert, but you can focus on improving communication in structured, low-pressure ways—such as writing clear documentation, preparing concise updates for stakeholders, or practicing short explanations of your work to non- technical audiences. Positioning yourself as someone who can translate complex technical detail into understandable insights can be just as valuable as being outspoken. You should also highlight adaptability, continuous learning, and curiosity about AI and new tools, framing yourself as forward- looking rather than resistant to change. Even small steps—like taking an online course in business analysis, practicing interview answers that show problem-solving impact, or learning prompt engineering basics—signal to employers that they’re evolving into the “analyst/programmer” or consultant type the market increasingly demands, without abandoning the core technical expertise that still underpins your strength. You’re Doing Better Than You Think In tough times, it's easy to feel like you’re falling behind. But if you're actively job searching, applying, or even just considering your next move, you're already making progress. Finding a job today isn’t just about luck. It’s about persistence, strategy, and being open to possibilities. Whether you are in-between roles, switching careers, or starting fresh, every step you take is a step forward. What Else You Can Do Right Now In addition to the above, here are some practical tips to help you stay focused and confident in your search: 1. Look for a career, not just a job Now is the perfect time to reflect on where you want to go long term. Look for roles that match your values, skills, and ambitions, not just your current needs. 2. Upskill and refresh your toolkit Short courses, certifications, and workshops can dramatically boost your employability. 3. Be flexible — and open-minded Part-time or contract work can often lead to permanent opportunities. Don't be afraid to explore new industries or ways of working. 4. Get expert support This is where we shine. PRA offers personalised advice, résumé help, interview coaching, and real market insights to help you stand out. The PRA Jobseeker Handbook We know the job search can feel overwhelming, so we’ve created a resource to make it easier. Our Jobseeker Handbook is a free, downloadable guide full of practical advice to help you: Build a standout résumé and cover letter Prepare for interviews with confidence Access the best job boards and training resources Map out your career goals and steps to get there Whether you're fresh out of university, returning to the workforce, or shifting careers, this guide will support you every step of the way. So don’t give up. Keep going. And when you're ready, we're right here beside you. At PRA, we don’t just place people into jobs, we help build careers. Professional, experts, fun and approachable that’s who we are, and how we show up for you. Article written by: Carrah Jordan | PRA Director – QLD P: +61730717200 M: +61483950845

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Can you believe 2024 is almost over? As we get ready to say goodbye to the year, let’s look back at some of the key talent trends that shaped the market in 2024 and are set to continue their influence into 2025. Spoiler alert: It’s all about speed, upskilling, and an experience that puts candidates first.