AI Technology & Platform Lead

About the Role:

We are looking for an AI Lead to build and scale a strategic AI technology platform for the Group. This is a senior, hands-on technology leadership role for someone who can combine deep technical credibility with the ability to shape enterprise-level strategy, build teams, and turn AI infrastructure into measurable business value.

The role will lead the development of a large-scale AI Factory / Token Factory, including GPU-based compute capacity to support model training, post-training, fine-tuning, and inference workloads securely, reliably, and cost-effectively.

You will also establish a common AI platform and model catalog that enables teams across the organization to discover, evaluate, deploy, and consume commercial and open-weight AI models through standardized interfaces.

Over time, the platform is expected to evolve from an internal enterprise capability into a commercially viable AI infrastructure and platform service for external customers.

This is an opportunity to help define and build one of the Group's most important next-generation technology platforms, working across GPU infrastructure, networking, storage, orchestration, model platforms, data integration, security, observability, and AI application enablement.

What You Will Do:

Shape the AI Technology Strategy

• Define and own the AI technology strategy and multi-year roadmap.
• Establish the target architecture covering AI infrastructure, AI platform, model services, orchestration, data integration, security, observability, and developer access.
• Continuously assess developments in GPUs, foundation models, inference technologies, agent platforms, and AI infrastructure.
• Translate emerging AI technologies into practical business capabilities.

Build the AI Factory / Token Factory

• Lead the strategy and development of a large-scale GPU-based AI Factory.
• Define capacity requirements for training, fine-tuning, post-training, and inference workloads.
• Drive architecture decisions covering GPU clusters, networking, storage, power, cooling, scheduling, virtualization/containerization, and high availability.
• Establish a scalable GPU roadmap beginning with an initial target of approximately 1,024 GPUs.
• Ensure high GPU utilization, appropriate workload prioritization, and competitive cost per token.

Establish the AI Platform

• Establish a common enterprise AI platform and model catalog that enables teams to discover approved models, evaluate model performance, deploy models safely, and consume AI capabilities through standard interfaces.
• Enable multiple model families including commercial APIs, open-weight models, multimodal models, embedding models, and specialist models.
• Establish common capabilities including:
• model gateway and model routing;
• inference serving;
• retrieval-augmented generation and enterprise knowledge integration;
• vector and knowledge services;
• fine-tuning and supervised post-training;
• model evaluation;
• guardrails;
• token metering and chargeback;
• observability;
• agent orchestration.

Enable the Organization

• Enable business units, engineering teams, data scientists, and product teams to consume AI capabilities through standardized APIs and self-service environments.
• Establish architecture standards, development patterns, and reference implementations.
• Build an effective AI engineering organization and develop internal AI capability.

Own Governance and Financial Discipline

• Own the AI technology investment plan, budget, capacity plan, and business case, including the economics of GPU utilization, cost per token, chargeback, and future external monetization.
• Establish capacity, performance, utilization, and unit-cost metrics.
• Work closely with cybersecurity, data governance, risk, privacy, legal, procurement, and finance functions to ensure the platform is secure, compliant, responsible, and financially disciplined.

What We Are Looking For:

The ideal candidate is a technology leader with strong depth across AI infrastructure and platform engineering, combined with the ability to influence senior stakeholders, build a new capability, and translate complex technology choices into business outcomes.

Essential Experience and Capabilities

• Significant leadership experience in AI infrastructure, cloud platforms, hyperscale infrastructure, advanced computing, or large-scale technology-platform environments, with accountability for architecture, delivery, reliability, and cost performance.
• Strong understanding of modern AI architecture, including GPU infrastructure, distributed systems, model serving, inference optimization, data integration, AI safety, and application enablement.
• Demonstrated experience designing and delivering complex technology platforms at scale.
• Good understanding of GPU computing, distributed systems, AI inference, AI model serving, cloud-native technology, and enterprise integration.
• Ability to lead both deeply technical teams and senior executive discussions.
• Strong technology strategy, architecture, financial, and vendor-management capabilities.
• Proven ability to build new technology organizations, operating models, engineering practices, and delivery capabilities in complex enterprise environments.

Preferred Experience

• Experience with NVIDIA GPU platforms, CUDA ecosystem, high-performance networking, and AI infrastructure.
• Experience with open-weight LLMs and multimodal models.
• Experience operating AI/ML platforms at significant scale.
• Experience commercializing internal technology platforms or operating cloud/platform services.
• Background in telecom, cloud, hyperscaler, large enterprise, or technology infrastructure businesses.

Why Join Us:

You will have the opportunity to build a new AI platform capability from the ground up, shape the Group's AI infrastructure strategy, and work on technology that will support both internal transformation and future commercial growth.

This role is suited to someone who wants to operate at the intersection of advanced AI technology, enterprise-scale platform engineering, and strategic business impact.