ML Engineers, there is an uptick in demand: Here’s What Clients Are Telling Us They Need
Over the past 3–4 months, we’ve seen an interesting uptick in demand for Machine Learning Engineers. It’s happening across fintech, healthtech, enterprise, and even government sectors. What’s interesting is that the brief has evolved. It’s no longer just about finding someone who can build models—it’s about finding engineers who can build models that actually deliver value and integrate seamlessly into business outcomes.
We have seen a rise in roles, particularly in Sydney and slightly in Melbourne, the trend is accelerating. Both enterprises and scale-ups are moving beyond experimentation and embedding ML into the core of their products and services. As one client put it recently, “We’re not just playing with ML—we need people who can ship real outcomes.”
After chatting with Engineering Managers, Heads of Data, and a couple of CTOs / Founders, some clear patterns have emerged. Technical depth remains non-negotiable. Clients are looking for engineers with strong fundamentals in algorithms, statistics, and optimisation, fluent in Python, and experienced with frameworks like TensorFlow, PyTorch, and Scikit-learn. But more than that, they want people who can deploy—those with hands-on experience using MLOps practices, Docker, Kubernetes, and cloud-native workflows to get models into production.
That said, tools and frameworks are just one piece of the puzzle. What really separates great ML Engineers is their ability to show outcomes. Hiring managers want to see a proven track record—tangible, real-world projects where they’ve helped boost retention, streamline operations, or improve product performance. One of the most common questions we hear is, “Can they show how their work made a real business impact?”
Communication skills are also becoming a big differentiator. The most in-demand ML Engineers don’t operate in silos. They collaborate closely with product teams, software engineers, and business stakeholders. They can take complex concepts and explain them clearly, keeping everyone aligned on the problem being solved. This ability to bridge the gap between technical depth and business context is incredibly valuable right now.
We’re also seeing a shift toward full-lifecycle ownership. Companies want engineers who can run with a project from start to finish—scoping the problem, preparing the data, building and training the model, deploying it, and monitoring its performance. The idea of being just a “model builder” is fading. These days, employers want full-stack ML capabilities, especially as their own ML maturity grows.
Curiosity and adaptability are two more traits that keep coming up in conversations. The ML landscape is evolving quickly, with new tools, frameworks, and ideas surfacing all the time—LLMs, foundation models, vector search, you name it. The engineers who are continuously learning, experimenting, and upskilling are the ones standing out. Clients love candidates who aren’t afraid to test, iterate, and learn fast.
Solid software engineering principles are now baseline expectations. Things like clean, reusable code, version control, unit testing, and documentation are being weighed heavily, particularly by teams building scalable, production-grade systems. It’s no longer enough to just know the data science—the engineering side really matters.
And finally, the biggest game-changer? Business alignment. The engineers our clients want are the ones who think commercially. They don’t just ask “Can we build this?”—they ask “Should we build this?” They understand trade-offs, know how to prioritise, and make sure their work connects directly to business strategy. That mindset sets them apart every time.
