Why AI Engineers Are So Hard to Find

Demand has exploded faster than talent can keep up. In Australia and globally, AI-related job postings have grown sharply—LinkedIn data shows AI hiring demand up well over 200% in recent years, and some reports suggest 15–20 open roles for every genuinely qualified AI candidate. At the same time, executives are under pressure to “do something with AI,” with more than 80% of Australian business leaders naming AI adoption a top priority for 2025. When every sector—finance, healthcare, retail, government—is chasing the same people, the market overheats quickly.​

Second, the bar for these roles keeps rising. It’s no longer enough to have played with models in a notebook. Companies want engineers who can design, train, and deploy reliable systems in production—owning data pipelines, deployment, MLOps, monitoring, and guardrails for “trusted AI”. Those skills aren’t built in a bootcamp or a single project; they take years of hands-on work with live systems. Tooling also changes fast: someone trained on last year’s stack may already need re-skilling to be effective on day one.​

Third, there’s a structural skills gap. Research shows demand for AI skills in Australia has grown around 20% per year since 2019, while wages for those skills have risen roughly 10–11% annually. Universities and corporate training simply haven’t scaled at the same pace. By 2030, Australia is projected to need well over 150,000 additional AI‑related specialists, across engineering, data, and applied roles.​

Finally, competition is global. Big tech, startups, consultancies, and even governments are all courting the same engineers, often with aggressive salaries, equity, and access to massive datasets. Smaller and mid-sized organisations—especially those outside pure tech—struggle to match those offers, even when they can offer meaningful work.​

For hiring managers, this means AI engineers aren’t just another requisition to fill; they’re a strategic constraint. Until organisations invest in clearer role definitions, faster hiring processes, and serious upskilling pathways, the talent bottleneck will remain one of the biggest brakes on real AI progress.​

‍