If you're worried about the impact of AI on the IT profession and thinking about which direction to take your career, the smartest answer is to focus on honing the skills that will help you become the latest, and perhaps greatest, enterprise AI expert of all: the frontier engineer.
The Rise of the Frontier Engineer
Steve Lucas, CEO of integration technology specialist Boomi, outlined how the frontier engineer — someone with an advanced degree in data and neural networking — will become the key professional who unlocks competitive advantage in the age of AI. In a conversation at his firm's World Tour event in London, Lucas emphasized that organizations will succeed when they have a deep understanding of how to optimize frontier models, how to use them, and someone has to think about those issues every day — and a CIO needs that person to be part of the organization.
Lucas knows more about the likely trajectory of the technology industry than most. CEO of Boomi since December 2022 and formerly CEO of iCIMS and Marketo, as well as a holder of senior executive positions at Salesforce and SAP, he is now helping the integration specialist and its customers to navigate effective data use in an era of agents. It's within this complex environment that Lucas said frontier engineers will bring clarity. However, IT professionals should be warned — this high-profile role requires specialist qualifications and involves challenging responsibilities. For this reason, frontier engineers will be in massive demand as companies seek to stay ahead of their rivals.
Cutting Through the Hype
Lucas set the rise of the frontier engineer in context. In many ways, the role is the latest in a long line of hyped-up AI-focused positions. However, the twist this time is that the frontier engineer will be a crucial position in the longer term, not just a short-term flash in the recruitment pan.
He turned first to prompt engineers, saying the role was once the flavor of the month for job seekers and the firms that wanted to employ talent: professionals got told all the time, article after article, video after video, that here is the best way to extract the answer out of a model. For over a year, the focus was prompt engineering. Then OpenClaw was released in late 2025, and the IT recruitment trend changed again: all of a sudden, the demand shifted to harnesses, referring to the operational software layer that helps AI models act reliably. This demand led to the rise of other roles: harness engineer jobs and more hyper-accelerated cycles.
More recently, loop engineering — the practice of designing, operating, and improving the feedback loops that let AI coding agents plan work — has become a must-have skill. Lucas noted that the growth-hacking crowd has latched onto 'loops,' and now job postings for loop engineers appear on platforms like Indeed.com. Shaking his head at this long line of short-lived roles, Lucas gave a particle physics-based description of the IT recruitment hype cycle: it's like quarks and bosons that pop into existence and then disappear. More worryingly for IT professionals following these cycles, the impact could be a shortcut to a career dead end. Those are not enduring skills; enduring skills are understanding data science and neural networking deeply.
Putting Knowledge into Practice
It's here — at the sharp end of enterprise AI deployment in data-led terms — that the frontier engineer role will make a different mark. Lucas traced the origins of the role to the demand for frontier model engineers at Big Tech firms that are pioneering AI innovations. These engineers are incredibly important, he stressed, noting that these talented individuals are also rare. He estimated that there are fewer than 3,000 people in the entire world, maybe even fewer than 2,000, who know how to build and train a model at the scale we're seeing today, where they understand neural networks, and they can build a backpropagation-oriented large language model.
As Lucas stressed, that's just 2,000 people globally — in North America, Europe, Asia, and everywhere else — who understand how neural nets generate answers to questions. However, even more crucially, a tipping point is being reached where more end-user organizations — not just technology behemoths — will need an incredibly strong engineer who can put his or her knowledge of the inner workings of frontier models into practice. Enterprises will need someone who really understands how neural nets work, not necessarily how to build them (that's rare), but how these things work, and how to best optimize them.
When asked what skills this frontier engineer would possess, Lucas said — at a minimum — an advanced degree in data and neural networking. He recognized that having strong capabilities across those two specialist domains is uncommon, especially when it comes to using this awareness in a non-technology business. It's one thing to understand data; it's also another thing to understand neural networks, which is an even rarer skill set. But the application of that capability in the enterprise is profoundly important.
Comparison with Other AI Roles
Lucas said the frontier engineer's responsibilities differ greatly from other senior positions, such as chief AI officer. A chief AI officer, in his view, is someone who is thinking about models, frameworks, compliance, integration with the broader organization — all those things that need to happen. To explain the requirements for the frontier engineer role, Lucas discussed the position of forward-deployed engineers, IT professionals who work closely with customers to ensure that technical solutions are deployed effectively in their operational environments. He referenced companies like Palantir that champion this role. A forward-deployed engineer is functionally different; that's someone who can iterate, build, and apply a solution to a business problem.
Somewhere in between the AI executive and the AI builder sits the frontier engineer — someone who knows enough about the finer details of the technology to exploit models effectively. But be warned: these people are hard to find, said Lucas. Even inside their own organizations, he knows this to be true. At Boomi, it's difficult to pin down the one person who is a deep expert on neural nets, and can she or he help squeeze every drop of productivity out of those things? However, Lucas said finding — or, for up-and-coming technology professionals, becoming — that rare individual is likely to be crucial and a pathway to long-term success: in the end, that capability will mean the difference between winning and losing.
The Path to Becoming a Frontier Engineer
The journey to becoming a frontier engineer is not for the faint of heart. As Lucas emphasized, it requires an advanced degree — typically a master's or Ph.D. — in data science and neural networking. This academic background provides the deep theoretical understanding necessary to grasp how neural networks learn, how they produce outputs, and how to tune them for specific enterprise use cases. Beyond academia, hands-on experience with large-scale model training and optimization is essential. Many frontier engineers come from roles at major AI labs, where they have worked on building or fine-tuning frontier models. However, given the extreme scarcity of these professionals, enterprises may need to invest in training their own talent or partner with specialized consultancies that offer deep neural network expertise.
Another avenue is the development of frontier engineering skills through dedicated bootcamps or internal training programs focused on advanced AI topics. While these alternatives cannot fully replace the depth of an advanced degree, they can help bridge the gap for promising candidates. Ultimately, the role demands not only technical prowess but also the ability to communicate the inner workings of AI models to business stakeholders. The frontier engineer must translate complex neural network behaviors into actionable insights for decision-makers, ensuring that the organization can use frontier models responsibly and effectively.
In the broader context of enterprise AI, the frontier engineer represents a strategic asset. As more companies adopt AI agents and autonomous processes, the need for someone who truly understands the underlying models will only grow. Without such an expert, enterprises risk deploying AI systems based on flawed assumptions or missing opportunities to squeeze additional performance from their investments. The frontier engineer becomes the linchpin that connects cutting-edge AI research with practical business value.
Given the high stakes, CEOs must ask themselves a critical question: is there one human in your company that understands how neural networks work? For 95% of organizations, the answer is likely no. Yet this is the fire that we are playing with. The frontier engineer provides the expertise needed to navigate this complex terrain, ensuring that AI initiatives deliver real returns. As the AI landscape continues to evolve at breakneck speed, the roles that endure will be those grounded in deep, transferable knowledge. The frontier engineer is such a role — one that promises to define the next generation of enterprise AI leadership.
Source:ZDNET News