The rapid evolution of artificial intelligence has created a new set of job titles, with two roles capturing the most attention: AI engineer and forward deployed engineer (FDE). As companies scramble to integrate AI into their operations, the question of which role provides the most business value has become a hot topic. Some industry leaders argue that the broader AI engineer position offers greater long-term potential, while others contend that the customer-facing FDE is indispensable for real-world implementation. The answer may depend on the organization's stage of AI maturity and the specific value it seeks to derive from these roles.

The rise of the forward deployed engineer

Forward deployed engineers are specialists who embed directly with customers to deploy and fine-tune AI systems. According to data from Henley Wing Chiu, CTO of Revealera, job postings for FDEs surged by 1,165% in 2025 compared to the previous year. This explosive growth reflects the increasing need for professionals who can bridge the gap between product development and real-world implementation. FDEs typically work on integrating AI models into existing workflows, handling data quality issues, and addressing security and compliance requirements.

Shruti Tyagi, senior manager of problem management at ServiceNow, highlights the value of this role: "Forward-deployed engineering is a strong path for people who want to work closer to real customer problems. 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." This hands-on approach helps organizations avoid the "last mile" problem where a perfectly built model fails to deliver business results because it cannot seamlessly integrate into daily operations.

Dan Herbatschek, CEO of Ramsey Theory Group, emphasizes that FDEs provide strategic direction. "AI engineers build the engine, but it is the forward-deployed engineer who is figuring out where that car should go. Looking to the future, knowing the destination is becoming far more valuable than knowing how the engine works." He argues that as AI development tools become easier to use, the ability to translate technical possibilities into business outcomes becomes the rarer and more valuable skill.

The case for the AI engineer

On the other side of the debate, prominent AI expert Andrew Ng, founder of DeepLearning.AI and co-founder of Coursera, makes a strong case for the AI engineer. In a recent post, Ng stated that the demand for AI engineers who can build software applications using LLM prompts, agentic frameworks, and AI coding agents is surging. He argues that FDEs often lock organizations into single vendors and models, whereas AI engineers operate with a broader toolkit and can adapt to changing technologies.

Ng's perspective is supported by Brandon Sammut, chief people and AI transformation officer at Zapier: "Andrew Ng frames this as a numbers question, and on the numbers, he's right. 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." For professionals optimizing for job availability, the AI engineer path appears to offer more opportunities.

Vasily Mazin, chief research officer at Mind Simulation Lab, adds that the AI engineer has a deeper technical foundation: "This specialist has a deeper understanding of the technology they'll eventually need to implement. It's simply a stronger foundation to build on. If an AI engineer also has strong communication skills and an analytical mindset, they can easily step into an FDE role and do well in it. Going the other direction—from FDE to engineer—is much harder." This suggests that the AI engineer role may provide more career flexibility and upward mobility.

Where the real value lies: Integration and impact

While the debate between the two roles continues, many experts agree that the most valuable professionals will be those who can combine technical depth with business acumen. Sammut notes that integration complexity is the number one barrier to making AI work in practice, ahead of budget or model quality. "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."

Herbatschek advises professionals not to optimize for a single title: "Don't optimize to become an AI engineer or a forward-deployed engineer. 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." This multidisciplinary approach is becoming increasingly critical as AI moves from experimental projects to enterprise-wide deployments.

Emergence of the human systems architect

A third category of roles is beginning to emerge that may prove equally important: the human systems architect. Ismail Amla, senior vice president at Kyndryl, describes this role as focusing on "designing how humans and AI work together." These professionals help determine where human judgment remains essential, how exceptions are handled, and how accountability is maintained as AI takes on greater decision-making responsibilities.

Amla points out that as much as 30% of critical decision logic exists as tacit knowledge rather than documented processes. This means that even the most powerful AI systems cannot fully replace the nuanced understanding that experienced employees bring to complex situations. Human systems architects are tasked with capturing that tacit knowledge and designing workflows that leverage both human and AI capabilities.

The skill set required for this role goes beyond technical expertise. It includes deep understanding of organizational behavior, change management, and ethical considerations. "The lesson is that AI expertise remains valuable, but the highest-growth opportunities may increasingly belong to those who can bridge disciplines," Amla says.

Industry trends shaping the future of these roles

The rapid pace of AI development is also changing what it means to be an AI engineer or FDE. Tools like Claude Code, Codex, and OpenCode are automating many routine coding tasks, allowing engineers to focus on higher-level design and problem-solving. This shift is making the traditional separation between building and deploying increasingly blurry. Some companies are already creating hybrid roles that combine elements of both positions.

Additionally, the rise of no-code and low-code AI platforms is lowering the barrier to entry for non-technical professionals. This may reduce the demand for pure AI engineers in some contexts, while increasing the need for forward deployed engineers who can guide business users in using these tools effectively. The debate between the two roles may soon become moot as the industry moves toward more integrated job functions.

Key takeaways for professionals

• Forward deployed engineer postings grew by 1,165% in 2025, indicating strong demand for customer-facing AI deployers.
• AI engineer roles are more numerous and offer broader technical foundations, according to Andrew Ng and other experts.
• The most valuable professionals will combine deep AI knowledge with business, finance, and operations expertise.
• A third role—human systems architect—is emerging to design how humans and AI collaborate.
• Integration complexity remains the top barrier to AI success, making skills in closing the gap between technology and business outcomes highly sought after.

As Tyagi sums up, an AI engineer may be the right path for someone seeking deeper technical specialization, while forward deployed engineering is great for someone who enjoys customer-facing problem-solving and connecting technical work to business outcomes. Ultimately, the choice between the two roles should be guided by personal strengths and the specific value one wants to bring to an organization. The debate will likely continue, but the need for professionals who can make AI work in practice is only going to grow.

The evolving landscape also highlights that exclusive focus on either role may be short-sighted. Companies are increasingly looking for adaptability rather than rigid job descriptions. The most successful AI professionals will be those who can pivot between building, deploying, and strategizing as business needs change. This flexibility, combined with a commitment to continuous learning, will be the true differentiator in the AI-driven job market of tomorrow.

Source: ZDNET News