The Rise of Forward Deployed Engineers in Applied AI

There’s a quiet shift underway inside applied AI teams. As companies move past demo culture and start wiring AI agents into real business processes, a different kind of role is rising to prominence—part engineer, part field operator, part architect, part empath.

For years, the function of a forward-deployed engineer (FDE) existed in different forms, useful mostly in tough government or industrial environments. But as AI agents begin speaking to customers, handling decisions and shaping frontline workflows, the need for engineers who understand the world outside the codebase has exploded.

Now, across AI companies, this once-niche model is making a strong comeback. Practitioners who’ve spent time in the field say this role is becoming critical to building AI systems that are safe in practice, useful in context, and tailored to the understated realities of each industry.

Applied AI’s Two Worlds

Sumanyu Ghoshal, product manager at Prodigal, says applied AI now operates across two parallel tracks. The first is product development—building the core system through orchestration, secure hosting and continuous fine-tuning. The second is deployment—getting these evolving AI systems to work reliably in real customer environments.

“The second piece is how you deploy the solution while you keep building the product,” he said. “That’s why FDEs, what we call agent engineers, matter.”

The title is still taking shape at Prodigal, but the role resembles the classic FDE: part solution builder, part product contributor.

Ghoshal experienced this firsthand as a forward-deployed engineering intern at Palantir, where the work blurred the line between customer-specific builds and core product development. “Whatever you build, a good chunk of it ends up influencing the product,” he said. That tight loop is especially valuable in AI, where systems must adapt to domain-specific needs.

It’s also essential for risk control. As Ghoshal noted, an AI agent can’t afford casual errors. “If an AI agent talking to a consumer gives a 90% discount or says something offensive, that’s a big problem for the business.” The stakes far exceed those of traditional software, making careful deployment and on-ground engineering oversight indispensable.

Companies look for specific qualifications and skills in FDEs. Neeti Sharma, CEO of TeamLease Digital, said, “Most companies look for a strong engineering background and three to eight years of real experience in software, data or applied ML.”

“They must know Python plus one backend language, and should be comfortable deploying systems on AWS, GCP or Azure, and familiar with APIs, microservices and DevOps practices.”

FDEs now also require practical expertise in AI technologies like LLMs, RAG and ML to successfully deliver working solutions. Beyond technical skills, top FDEs are distinguished by clear communication, strong product thinking and the ability to handle ambiguity.

Context may make or break AI Agents

Alex Hill, director of applied AI at Celonis, believes meaningful AI systems can’t be built from a distance. “In the state AI is in today, you cannot build solutions that move the needle from your headquarters,” he said. The gap is simple: no internal discussion can replicate the reality of someone working on a manufacturing line or inside a plant.

Celonis didn’t plan for a forward-deployed engineering model; it arrived there by necessity. Its most effective deployments came from sending strong technical builders on-site to shadow end users, observe workflows, build quick prototypes and iterate immediately. “They build a solution in one or two days,” Hill said. Some attempts fail, but rapid, real-world feedback tightens the loop—eventually resembling an FDE model without the label.

For Hill, the key is context. The industry has moved past prompt engineering into what he calls context engineering: giving AI agents the depth of historical, operational and relational information that humans rely on. Without this foundation, AI cannot meet enterprise standards or operate safely. Context spans vendor relationships, past decisions and subtle process cues—“at least what the human knows, and often more.”

Where the Role Goes Next

FDEs are emerging as the link between product teams and real-world workflows. They bring empirical grounding to product decisions and expose model limitations early.

Omkar Pandharkame, chief strategy officer at Supervity AI, describes them as hybrids with customer-facing instincts and AI-first development skills. “An FDE is not a rebranded solutions engineer; they need a deep understanding of workflows and the agility to build agents in days, not months.”

Highlighting the rise in the demand for the role, Teamlease’s Sharma said, “Demand for FDEs has risen sharply as enterprises move from AI pilots to real, scalable deployments. The challenge today isn’t accessing AI models, but integrating AI into complex workflows, legacy systems and real user environments. FDEs are the bridge that makes this possible.”

She noted that the demand for FDEs is experiencing substantial growth, with high double-digit increases in job demand during the first three quarters of 2025 alone.

Unlike traditional developers, FDEs work in short, high-impact cycles, building fast prototypes that prove value on the ground. As Pandharkame put it, the role “sits at the intersection, one part consultant, one part generative AI engineer.” Companies like Supervity AI are hiring aggressively, viewing the role as the next evolution of pre-sales engineering.

Experts broadly agree as AI agents integrate deeper into business processes, the demand for engineers who can work directly with customers will only rise. Far from being automated away, the FDE may become one of the defining roles of enterprise AI.

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