Global In-house Centres (GICs) are entering a new phase in 2026. They are no longer being built simply to provide cost-efficient support or execute routine business processes. Increasingly, GICs are becoming strategic hubs for artificial intelligence, product engineering, data, automation, cybersecurity, and enterprise transformation.
India is at the centre of this shift. The country had 2,117 GIC/GCC centres and approximately 2.36 million professionals as of FY2026, with AI and technology capabilities becoming an increasingly important part of their mandates.
For organisations planning to establish or expand a GIC in India, this means the hiring strategy must also change. Traditional technology hiring alone is no longer enough. Businesses need specialised GenAI talent capable of building, deploying, governing, and integrating AI into real business processes.
So, which roles should be part of a modern GIC hiring plan in 2026?
1. Generative AI Engineer
The Generative AI Engineer is becoming one of the most important roles within an AI-focused GIC. These professionals build applications powered by large language models (LLMs), develop AI workflows, integrate models with enterprise systems, and create solutions for functions such as customer service, finance, legal operations, healthcare, and knowledge management.
A strong GenAI Engineer should understand Python, APIs, LLM frameworks, vector databases, retrieval-augmented generation (RAG), prompt engineering, and cloud-based AI platforms.
For a GIC, this role can transform AI from an experimental initiative into a scalable enterprise capability.
2. AI/ML Engineer
While GenAI receives much of the attention, traditional AI and machine learning remain critical. AI/ML Engineers develop predictive models, recommendation systems, classification models, forecasting solutions, and intelligent automation.
These professionals can help GICs apply AI to areas such as fraud detection, demand forecasting, risk management, operational analytics, customer insights, and process optimisation.
AI/ML engineering is already among the priority skill areas for GIC hiring, alongside GenAI, data science, cybersecurity, and cloud.
3. Prompt Engineer / AI Workflow Specialist
Prompt engineering has evolved beyond simply writing effective instructions for an AI model. In modern enterprises, AI workflow specialists design repeatable interactions between employees, AI models, enterprise data, and business applications.
They can develop structured prompts, AI-assisted workflows, evaluation processes, and automation frameworks that improve productivity while maintaining consistency.
For GICs, this role can be particularly valuable when introducing GenAI across finance, HR, customer operations, legal services, research, and technology teams.
4. AI Product Manager
Technology teams can build powerful AI systems, but someone must ensure those systems solve the right business problems.
That is where the AI Product Manager comes in.
AI Product Managers connect business objectives with AI capabilities. They identify use cases, define product roadmaps, prioritise AI initiatives, measure business impact, and coordinate between engineering, data, design, compliance, and business teams.
As GICs increasingly move toward product ownership rather than pure service delivery, AI product management is becoming an important capability.
5. MLOps / AI Platform Engineer
Building an AI model is only the beginning. Enterprises need systems that can deploy, monitor, update, secure, and scale those models.
MLOps and AI Platform Engineers are responsible for creating the infrastructure that makes this possible. Their work can include model deployment, monitoring, CI/CD pipelines, cloud infrastructure, model versioning, data pipelines, and performance optimisation.
For a GIC supporting global operations, this role is essential for moving AI projects from proof-of-concept to production.
6. AI Governance & Responsible AI Specialist
As organisations adopt GenAI at scale, governance cannot be an afterthought.
AI Governance Specialists help organisations establish policies around responsible AI, data usage, model risk, privacy, transparency, security, compliance, and human oversight.
This role becomes especially important when GICs handle sensitive enterprise information or build AI solutions used across multiple markets.
A mature GIC should therefore think about AI governance at the same time it thinks about AI development—not after deployment.
7. AI Data Engineer
GenAI is only as effective as the data supporting it.
AI Data Engineers build the data pipelines, architectures, integrations, and processing systems required to make enterprise information accessible to AI applications. They work with structured and unstructured data and help prepare information for analytics, machine learning, and LLM-based applications.
Their work is particularly important for Retrieval-Augmented Generation (RAG), enterprise knowledge systems, AI agents, and intelligent automation.
As GICs take greater ownership of enterprise data and technology platforms, demand for professionals who can connect data engineering with AI is likely to remain strong.
8. AI Security / AI Forensics Specialist
The expansion of GenAI also creates new security risks.
AI Security Specialists focus on protecting AI models, applications, data, and workflows against threats such as prompt injection, data leakage, model manipulation, unauthorised access, and misuse of AI systems.
AI forensics is another emerging area, involving the investigation of suspicious AI activity, model behaviour, and potential security incidents.
This capability can become increasingly important as GICs move from experimenting with AI to managing mission-critical AI systems.
Why These Roles Matter for GICs in 2026
The biggest change in GIC hiring is the move from volume to capability density.
GICs are increasingly expected to own products, platforms, advanced technology initiatives, and enterprise transformation rather than simply execute tasks designed elsewhere. Recent industry research also indicates that GCC hiring is becoming more specialised, with companies prioritising experienced professionals who combine technical expertise with business and domain knowledge.
At the same time, the supply of specialised AI talent remains challenging. Some GICs are responding by reskilling existing employees and moving professionals from adjacent technology roles into AI, cloud, cybersecurity, and platform engineering positions.
This means companies establishing a GIC should not rely entirely on external recruitment. A stronger strategy combines specialist hiring, internal reskilling, leadership development, and long-term talent pipelines.
Build Your GIC Hiring Plan Around Skills, Not Just Job Titles
A successful GIC in 2026 needs more than a list of vacancies. It needs a clear talent architecture.
Before hiring, organisations should identify which AI capabilities must be owned internally, which functions can be developed through reskilling, and which specialised skills may require targeted recruitment.
It is also important to define career paths for AI professionals. Research from the 2026 Infosys AI-First GCC Index found that GICs/GCCs with clearly defined AI roles and career pathways were 19% more likely to report significantly improved AI outcomes.
The objective should be to build an AI-ready organisation rather than simply fill AI-related positions.
The Future of GIC Hiring Is AI-First
GenAI is changing what companies expect from their Global In-house Centres. The next generation of GICs will increasingly combine engineering, data, AI, automation, cybersecurity, domain expertise, and product ownership.
The eight roles discussed above—Generative AI Engineer, AI/ML Engineer, Prompt Engineer, AI Product Manager, MLOps Engineer, AI Governance Specialist, AI Data Engineer, and AI Security Specialist—can form the foundation of a modern GenAI hiring strategy.
For organisations planning to set up a GIC in India, the opportunity is significant. India offers a deep technology talent ecosystem, established GIC infrastructure, and growing expertise in AI and digital engineering. But success will depend on designing the right workforce from the beginning.
At GIC-in-India.com, we help organisations explore and build their India GIC strategy with the right approach to talent, operations, infrastructure, and scalability.
Planning to set up or expand a GIC in India in 2026? Build your AI talent strategy before you build your team. Contact GIC-in-India.com to explore your next step.
