Indian Engineers are Jack of All Trades, But FDE Roles Demand Master of AI
By Ajay Rag ·
As Indian enterprises race to move artificial intelligence from pilot projects to full-scale production, a new talent bottleneck has emerged. The job is highly sought-after, considerably well-paid, but requires skills that are not as easy to hone or even upskill.
The role in question is that of the Forward Deployed Engineer (FDE), once a niche Palantir job title, now rapidly becoming, in the words of one industry tracker, "an enterprise delivery model." An FDE ensures that propreitary AI models actually work inside messy, legacy-laden corporate environments. The ask is to not only commit code but also set up cloud infrastructure, build APIs, and deploy working solutions.
The steep skilling ask has, in part, led to a talent deficit.
According to research by consulting firm Han Digital Group, India's software engineering talent pool stands at roughly 3 million, with AI talent nearing 1 million. There was a requirement of 25,000 FDE roles across the IT-BPM industry in the first two quarters of FY27 alone. However, Xpheno research notes that there was a potential talent pool of only 2,800.
And India, despite housing the world's largest technology workforce, is discovering that it cannot hire its way out of the shortage. It will have to build the talent itself.
From Niche to Mainstream
IT services firms are leading the charge to accelerate AI deployment. Roughly 5% of all AI engineering roles now carry an explicit FDE mandate, per the Han Digital Group research. According to research firm UnearthInsight, India is now home to 7,000 such roles in the broader deployment engineering pool.
They follow the lead of OpenAI and Anthropic, which launched new enterprise deployment companies earlier this year backed by multi-billion-dollar venture capital to embed frontier AI models within enterprises. Microsoft, Google Cloud, and AWS followed suit.
"The problem has changed," Gaurav Vasu, Founder and CEO of UnearthInsight, tells AIM. "Enterprises already have access to models, but they are struggling to put them into production across thousands of applications, legacy systems, security rules, and business processes."
He calls this the last-mile problem of enterprise AI.
Capability Gap
Experts are emphatic on one point. India is not short of engineers. But it does have a dearth of developers who can boast having a very specific combination of skills.
The pool of developers who can work at the intersection of writing production-grade code, understanding LLM and AI architectures, navigating enterprise data and legacy systems, grasping a customer's business problem, and communicating with senior stakeholders, while owning outcomes end-to-end, is vanishingly small.
"The market is trying to hire a unicorn because companies have packaged nine different capabilities into one job description. They want someone who can be a software engineer, AI engineer, solution architect, cloud engineer, consultant, domain expert, and customer-facing delivery lead simultaneously. That dramatically reduces the available talent pool," says Saravanan Balasundaram, Founder and CEO of Han Digital Group.
“The bigger problem is that the number of people who are satisfied with the full FDE mandatory skill combination is much smaller than the number who satisfy any one or two of the FDE mandatory skills,” he adds.
Unearthinsight’s data suggests the gap is roughly three times global demand versus experienced supply, widening to 4–5x in the most contested segments. Hyperscalers alone have around 6,000 open FDE roles chasing just 1,200 people who genuinely fit the profile.
According to Vasu, around 90% of LinkedIn profiles bearing the FDE tag belong to offshore functional consultants or remote-support engineers rather than professionals with real forward-deployed, client-embedded experience.
Separately, UnearthInsight's research suggests 75–80% of Indian jobs currently labelled FDE are, in substance, closer to solutions or implementation engineering roles.
"India does not have to create 20,000 FDEs from scratch," Vasu suggests. "It has to convert its existing engineering, services, and GCC (global capability centre) talent into FDEs."
Ashish Kumar Gupta, Global Head of the New Business Incubation at HCLTech, situates the FDE phenomenon within a longer arc of technology convergence.
Cloud computing, he says, already pushed engineers to become more multifunctional over the past decade and AI is merely accelerating that trend. But he cautions that the FDE model only works when an organisation's product and engineering functions are tightly consolidated at the back end before being "deployed" to the frontend with customers.
"You can't do FDE unless you've got your backend sorted out first," Gupta says, describing it as a structural, not merely a hiring challenge.
Inculcating Domain Knowledge is Hard
Industry experts push back against the assumption that LLM fluency is a scarce ingredient.
"The hardest capability to find is not prompt engineering or even basic LLM development," Balasundaram says. "It is the ability to take an AI system from a successful prototype to a reliable production system inside a complex enterprise environment", which increasingly demands skills in evaluation frameworks, observability, guardrails, security, and production monitoring.
Further, Vasu points to industry-specific domain knowledge as India's biggest structural gap.
Technology careers here have traditionally been built around pure engineering skill, not deep vertical expertise in, say, oil and gas or banking and insurance. An FDE, he argues, needs to build the technology, understand the industry's business processes, and understand how work actually happens inside a specific company—a rare combination of context that erodes quickly if engineers are rotated between projects too often.
Reskilling Strategy
With no mature, decade-deep pool of career FDEs to draw from, industry executives agree that companies must build the talent internally rather than buy it externally. Vasu sees India's advantage lying precisely in the coexistence of these skills within its ecosystem—three decades of client-facing engineering experience in IT services, deep process knowledge inside GCCs, and newer AI-native engineering talent emerging from startups.
With a skewed demand-supply gap, companies are trying to lure scarce FDE talent with considerably higher pay.
Per Han Digital Group, an FDE with four to eight years of experience can command ₹25–50 lakh per annum, with that figure rising to ₹55–95 lakh or more at senior levels. Vasu estimates FDE pay runs 30–60% above comparable engineering bands.
The Road Ahead
Industry watchers largely agree that deployment capacity may well set the pace of enterprise AI adoption over the next two to three years. Enterprises can readily buy access to frontier models from OpenAI, Anthropic, Google, Microsoft, or open-source alternatives. What they cannot easily buy—even with lucrative pay—is the talent to connect those models to real data, workflows, security systems, and employees, and stay accountable until the outcome is delivered.
India, then, has an opportunity to build a pipeline of FDE talent. For that, it needs to systematically reskill software engineers, AI specialists, architects, and consultants the country already has in abundance.