Enterprise Architect - AI

World Wide Technology is looking for a deeply technical Enterprise Architect who will own the delivery of AI projects end to end from the silicon and data centre design that underpins AI workloads, through the software and MLOps stack, to the governance frameworks that make AI trustworthy and defensible at scale. This is a technical hardware-and-software architect role, not a strategy-only position. The successful candidate operates comfortably across GPU infrastructure, high-performance networking, model training and inference pipelines, and the AI risk/governance disciplines increasingly demanded by regulators and enterprise boards.

The Enterprise Architect will lead technical delivery teams for client engagements, acting as the single point of technical accountability from design through to go-live, while mentoring delivery teams and shaping WWT's broader AI point of view.

Key Responsibilities

• Own end-to-end technical delivery of AI/ML engagements: architecture definition, design authority, build oversight, and go-live validation.
• Host and chair Architecture Review Board (ARB) and Technical Design Authority (TDA) sessions for AI engagements, owning governance gates, decision records, and design sign-off.
• Architect AI infrastructure spanning GPU/accelerator compute, high-performance interconnects, parallel/high-throughput storage, and orchestration.
• Design the AI software stack: training and fine-tuning pipelines, distributed training frameworks, inference/serving platforms, MLOps/LLMOps tooling, vector databases, and retrieval-augmented generation (RAG) and agentic architectures.
• Define AI governance frameworks covering model risk management, responsible AI, data lineage, bias/fairness testing, explainability, and regulatory alignment (EU AI Act, NIST AI RMF, ISO/IEC 42001).
• Act as trusted technical advisor to client CTOs, CIOs and Heads of Data/AI on platform strategy, build-vs-buy decisions, and AI operating model design.
• Lead technical workshops, architecture design sessions, and proof-of-concept builds with cross-functional engineering, data science, and security teams.
• Serve as the technical escalation point for delivery teams; unblock design and implementation issues under time pressure.
• Mentor other architects and engineers on AI systems design, uplifting AI depth across the practice.
• Partner with sales and pre-sales to scope AI solutions, size infrastructure, and validate technical feasibility of proposed architectures.
• Define automation, orchestration, and observability standards across the AI stack, from GPU cluster provisioning through to model monitoring in production.
• Architect integration points connecting AI platforms to existing enterprise networks, third-party systems, and external or service-provider-hosted environments.
• Track the fast-moving AI landscape — new model architectures, silicon, frameworks and regulation — and translate relevant developments into WWT's delivery methodology and client recommendations.

Required Technical Skills & Experience

• 10+ years in enterprise architecture, infrastructure engineering, or platform engineering roles.
• 5+ years focused specifically on AI/ML systems design and delivery, including at least 2 years working with generative AI/LLM workloads.
• Demonstrated track record leading technical delivery on enterprise-scale AI or HPC infrastructure programmes.
• Experience with GPU/accelerator architectures, high-performance networking, data centre facilities, and storage for AI compute.
• Hands-on experience with ML frameworks, distributed training, inference & serving platforms, and MLOps/LLMOps.
• Knowledge of automation, orchestration, and observability tools for AI systems.
• Experience with API-based and event-driven integration of AI platforms with enterprise systems.
• Working knowledge of model risk management frameworks and responsible AI principles.
• Familiarity with data privacy regulation as applied to AI training and inference data.
• Proven experience hosting and chairing formal Architecture Review Board and Technical Design Authority forums.
• Excellent executive communication and presentation skills.

Certifications

• NVIDIA certifications (NCP-AI Infrastructure, or NVIDIA Deep Learning Institute credentials) — strongly preferred.
• Cloud AI/ML certification: AWS Certified Machine Learning – Specialty, Microsoft Certified: Azure AI Engineer Associate, or Google Professional Machine Learning Engineer — at least one preferred.
• Kubernetes: CKA or CKAD — preferred.
• TOGAF 9/10 or equivalent enterprise architecture certification — beneficial.

Education & Experience

• Bachelor's degree in Computer Science, Computer Engineering, or a related technical field, or equivalent demonstrable experience.
• Advanced degree (MS in CS/AI/ML or related) beneficial but not required given sufficient hands-on depth.
• Continuous, demonstrable learning in AI — publications, open-source contributions, conference speaking, or lab-based experimentation are all valued signals.

WWT is an Equal Opportunity Employer. Employment decisions are made without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status or other characteristics protected by law. We are committed to working with and providing reasonable accommodations to individuals with disabilities.