AI engineer vs. forward deployed engineer: Which role delivers the most business value?

Follow ZDNET: Add us as a preferred source on Google.ZDNET's key takeawaysPostings for forward-deployed engineers grew by 1,165% last year.Some AI leaders say AI engineers are in a more valuable role.Ultimately, the role that matters is one that brings value to the business.You may have been hearing a lot of buzz lately about the role of forward deployed engineer (FDE) as a career option. But how viable an option is it? Among industry experts, opinions are mixed.The number of job postings with the job title "forward deployed engineer," tracked through 2025, grew by 1,165% over the previous year, according to estimates compiled by Henley Wing Chiu, chief technology officer of Revealera. Top responsibilities of FDEs include working directly with customers, building and deploying AI and machine-learning systems, and integrating systems and APIs.Also: Rolling out AI agents? 4 ways to move fast and furious - but with extreme cautionFDEs embed themselves with customers and users, helping promote and implement AI. "Forward-deployed engineering is a strong path for people who want to work closer to real customer problems," said Shruti Tyagi, senior manager of problem management at ServiceNow. "In enterprise AI, the challenge is often not just building the AI solution. It is making it work inside existing workflows, security requirements, approval processes, data issues, and adoption challenges." However, one prominent AI expert says FDEs have limited roles, and the broader emerging category of AI engineers has the most career potential for tech professionals. AI engineers are actually where AI-driven job growth is taking place, argues Andrew Ng, founder of DeepLearning.AI, chairman and co-founder of Coursera, and an adjunct professor at Stanford University.FDEs may lock organizations into single vendors and models, whereas AI engineers operate within a broader realm, Ng stated in a recent post. "Right now, I see surging demand for AI engineers who can build software applications using AI software components (like LLM prompts, agentic frameworks, evals, etc.) and effectively use AI coding agents (like Claude Code, Codex, Antigravity CLI, and OpenCode)."Also: The autonomous business is coming. Here's why that shift is good news for professionalsLeaders across the industry agree in principle with Ng's premise, but caution that the AI space is changing so rapidly that it's difficult to calibrate one's skills. "Andrew Ng frames this as a numbers question, and on the numbers, he's right," said Brandon Sammut, chief people and AI transformation officer at Zapier. "There will be more AI engineer roles than FDE roles, because most companies want their own people building their own systems rather than a few embedded specialists. If you're optimizing for how many open jobs exist, AI engineer wins."AI engineer is the better path as "this specialist has a deeper understanding of the technology they'll eventually need to implement," agreed Vasily Mazin, chief research officer and co-founder at Mind Simulation Lab. "It's simply a stronger foundation to build on. If an AI engineer also has strong communication skills, the ability to explain complex things clearly, and an analytical mindset that lets them see where AI solutions fit into a company's specific problems...they can easily step into an FDE role and do well in it, ideally without losing their technical edge and keeping a pulse on how AI is evolving. Going the other direction -- from FDE to engineer -- is much harder." Not everyone agrees that FDEs have a limited scope, however. "AI engineers build the engine, but it is the forward-deployed engineer who is figuring out where that car should go," said Dan Herbatschek, CEO and founder at Ramsey Theory Group. "Looking to the future, knowing the destination is becoming far more valuable than knowing how the engine works. Part of this reason is that AI is just getting easier to build. Each month, the models are getting better, the tools are easier to use, and most of the heavy lifting on the technical side is automated. FDEs are valuable because they sit at the intersection of tech, operations, and business outcomes."Also: Why AI tokens will send your enterprise cloud bill sky-high againAt the same time, "don't optimize to become an AI engineer or a forward-deployed engineer," Herbatschek advises. "Optimize to become irreplaceable by learning AI deeply, but also have a strong background in finance, operations, product, customer experience, and organizational change. The highest salaries will be for those individuals who know how to make models into ROI." Sammut also pushed back on the premise that one role may be more valuable than another. "Integration complexity is the number one barrier to making AI work in practice, ahead of budget or model quality," he said. "Whether your title says FDE or AI engineer, the person who can close that gap keeps getting hired. It's the ability to sit with a team, figure out what they're actually trying to solve, and build something that survives contact with their real systems. That's the skill in short supply."An AI engineer may be a suitable career pursuit for "someone who wants deeper technical specialization," said Tyagi. "Forward-deployed engineer is a great path for someone who enjoys customer-facing problem-solving, ambiguity, and connecting technical work to business outcomes." The debate between FDE and AI engineer may even grow mute as AI progresses. 'The debate assumes that the future of AI will be defined primarily by building and deploying models," said Ismail Amla, senior vice president of Kyndryl Consult at Kyndryl. "In reality, a third category of roles is emerging that may prove just as critical: designing how humans and AI work together." Also: How to build better AI agents for your business - without creating trust issuesSuch a role -- what Amla calls a human systems architect -- is tasked with helping determine "where human judgment remains essential, how exceptions are handled, and how accountability is maintained" as AI takes on greater decision-making. Importantly, "as much as 30% of critical decision logic resides as tacit knowledge rather than documented processes," he added. "The lesson is that AI expertise remains valuable, but the highest-growth opportunities may increasingly belong to those who can bridge disciplines."