Why FDEs Are Emerging as a Critical GCC Role

Forward-deployed engineers (FDEs) are emerging as a key talent category in India’s global capability centres (GCCs), as companies shift from building AI models and running pilot projects to integrating AI into enterprise workflows.

In June 2026, Emids, the US-headquartered healthcare technology and digital engineering company, announced plans to hire 1,000 forward-deployed context engineers over 12 months to support enterprise AI implementation in healthcare. These roles are closely related to FDEs, but the company calls them FDCEs. 

India has more than 1,900 GCCs employing over 2.1 million professionals, according to ANSR’s September 2026 report, Talent Trends in GCCs in India. The report found that postings for forward-deployed engineering and AI-engineering roles rose roughly 1,000% over 12 months. 

Nearly two-thirds of new GCC roles in 2026 require AI skills, while demand for AI and data talent increased around 45% year-on-year.

Separate data from NASSCOM Community, published in August 2026, points to a similar surge in global demand. FDE job postings rose approximately 729% year-on-year, reaching around 5,330 open roles in April 2026.

The hiring figures reflect a broader change in what companies expect from engineering teams. As AI moves into production, organisations need professionals who can connect models with business processes, integrate them into existing technology systems and ensure they deliver measurable results.

Unlike conventional engineering roles that may focus on building individual components, FDEs work at the intersection of software engineering, AI, product development and business consulting. Their responsibility extends from understanding a business problem to adapting technology to the client’s environment and ensuring the solution works in practice.

“As the model improves, the FDEs again will be needed in the same firm to actually improve the workflows further, which they may have set up, let’s say, a couple of years back,” said Amar Srivastava, CEO of Online Business and Group CPO at Scaler, tells AIM.

GCCs move from technology support to workflow ownership

The growing relevance of FDEs comes as GCCs take on responsibilities beyond traditional IT support, maintenance and execution. Many are increasingly expected to develop products, build platforms, and integrate AI into their parent organisations’ operations.

Srivastava argues that engineering’s future will increasingly involve two areas: improving the capabilities of AI models and integrating those models into enterprises to create business value. FDEs are positioned to play a central role in the second area.

“GCCs will have to evolve,” he says, adding that centres will need people who can understand how models developed by frontier and sovereign AI labs can improve enterprise workflows end to end.

For GCCs, this could mean greater responsibility for translating global AI strategies into working systems. Rather than simply receiving a model or platform from headquarters, engineering teams in India could help identify use cases, connect the technology to internal systems, resolve implementation challenges and improve deployments based on operational feedback.

For instance, Abhishek Sharma, Principal Software and AI Engineer at Fidelity International, works at the intersection of enterprise platform modernisation and AI engineering. His role involves collaborating with product, business and engineering teams to redesign enterprise platforms while building AI agents for business analysis, architecture and validation. His team is also developing a context engine that gives AI agents a shared understanding of enterprise requirements, standards and objectives.

 “The interesting part is that those two streams feed each other — the platform work shows us where agents genuinely earn their place, and the agent work makes the platform work more disciplined,” Sharma said.

Working in a global capability centre (GCC) has also shifted Sharma’s role from delivering individual components to influencing global product and engineering decisions. His team connects business, product, operations and technology across markets, balancing global consistency with local requirements. The experience has placed him at the intersection of enterprise capabilities and product delivery, while allowing his team to feed proven learnings and reusable solutions back into the wider organisation.

The challenge is getting AI into production

The growing demand for FDEs also reflects a persistent problem in enterprise AI: a successful pilot does not necessarily translate into a reliable production system.

Models must work with existing applications, legacy infrastructure, internal data, security controls and regulatory requirements. Businesses also need to determine how AI systems should handle exceptions and interact with human teams.

Vibhor Nigam, Senior FDE at Databricks, describes the role as combining technical implementation with customer-facing problem-solving.

“As FDEs, we sit at an intersection of software, product or AI Engineering and business consulting. We are the trusted advisor for the client’s engineering teams and are responsible for making the client successful on a company’s product,” Nigam says.

He adds that an FDE’s job goes beyond building a solution to earning client trust and shaping how the customer uses the product or platform.

Anubhav Pandey, FDE at IBM, further states that the role also accelerates the feedback loop between customer requirements and product development.

“We compress the feedback loop between customer pain points and product roadmap from months into days,” Pandey says.

FDEs work within client infrastructure to adapt products to specific enterprise requirements, including security perimeters, compliance constraints, legacy technology stacks and real-world data pipelines.

“Because these deployments are inherently complex and tailored to each enterprise’s architecture, off-the-shelf software solutions often fail without skilled FDEs to bridge the integration gap on the ground,” he states.

This becomes more important as companies deploy AI agents that can use tools and interact with multiple enterprise systems. Such deployments require teams to connect models to business applications, establish appropriate access controls and ensure automated workflows operate within defined boundaries.

FDEs can help bridge these gaps while feeding lessons from real-world deployments back to product and engineering teams.

Not every business process is ready for AI

However, adding FDEs to an engineering team does not automatically make an organisation ready for AI. The underlying business processes and data must be sufficiently understood for the technology to deliver value.

“As of today, when we talk about FDEs, I think the FDEs can only add value if the firm has a process which is very defined,” Srivastava states.

He identified three conditions for successful deployment: relevant data must exist, workflows and standard operating procedures must be defined, and interaction with the physical world should be minimal.

When these conditions are met, FDEs can structure imperfect data, integrate AI models, and improve existing workflows. Where data is unavailable, or processes are poorly defined, implementation becomes more difficult.

Customer support is one example. Companies often have established knowledge bases, support tickets and documented procedures that AI systems can use to automate parts of customer interactions.

The same principle can apply to GCCs serving sectors such as healthcare. AI may help streamline administrative work and back-office processes, while doctors retain clinical decision-making.

“Everything that you can imagine was happening with, let’s say, structured data can now be automated with AI, but it will need people who are very good with AI systems, who understand business logic and can actually solve this problem,” Srivastava says.

A shift in GCC hiring priorities

The rise of FDEs points to a shift in GCC team composition, with greater emphasis on experienced professionals who combine engineering expertise with business understanding. 

ANSR’s report found that GCC hiring is concentrated in mid-career talent: approximately 62% of hires were professionals with three to eight years of experience, with a median of six years.

The emerging requirement is not simply for engineers who can write code or use AI tools. It is for professionals who can understand a business problem, select and integrate the right technology, and take responsibility for getting it to work in a real operating environment.

Still, the FDE role is unlikely to be equally relevant to every GCC. Its value is greatest where companies have complex workflows, usable data and a clear need to integrate AI into existing operations. Organisations with fragmented data or poorly documented processes may need to address those foundational problems first.

The larger question is whether FDEs remain a specialised deployment function or become a core part of enterprise engineering teams. As AI models improve, companies will need to revisit existing workflows, adapt systems to new capabilities and continuously refine deployments.

For GCCs, that could make FDEs an important part of the transition from centres that execute technology work to centres that build, integrate and operate AI-driven business systems.