Enterprise Architect - AI in London

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.

AI Hardware & Data Center Infrastructure

• GPU/accelerator architectures: NVIDIA / AMD, including multi-node scale-out design.
• Accelerator interconnects: NVLink, NVSwitch.
• High-performance networking: InfiniBand and RoCEv2 fabric design, 400G/800G Ethernet, rail-optimized topologies for AI clusters.
• Data centre facilities: power density, liquid cooling, and rack-level design considerations specific to AI compute.
• Storage: parallel and high-throughput file systems sized for training and checkpointing workloads.

AI Software, MLOps & Generative AI

• ML frameworks: PyTorch and TensorFlow at a working, hands-on level.
• Distributed training: Horovod, DeepSpeed, Megatron-LM, or equivalent multi-node training frameworks.
• Inference & serving: NVIDIA Triton, vLLM, TensorRT-LLM, or equivalent high-throughput serving platforms.
• MLOps/LLMOps: Kubeflow, MLflow, and at least one hyperscaler ML platform.
• Generative AI: LLM fine-tuning, RAG architecture design, vector databases, and agentic frameworks.
• Data pipelines: data lake/lakehouse architectures, ETL/ELT, and data quality/lineage tooling that feed AI systems.

Automation, Orchestration & Observability

• Infrastructure-as-code: Terraform and Ansible for repeatable, automated provisioning of GPU clusters and AI platform environments.
• Pipeline orchestration: Kubeflow Pipelines, Apache Airflow, or Argo Workflows.
• Cluster & workload scheduling: Slurm, Run:ai, and NVIDIA Base Command Manager for GPU job scheduling.
• CI/CD/CT for ML: automated model testing, validation gates, and promotion pipelines.
• Infrastructure & GPU observability: NVIDIA DCGM, Prometheus/Grafana, and related telemetry stacks.
• Model & LLM observability: production model performance monitoring, data/concept drift detection, and LLM-specific observability.
• Logging & tracing: centralized logging and distributed tracing across data, training, and inference pipelines.

Integration — AI Stack, Enterprise Networks & Service Provider Environments

• Platform integration: API-based and event-driven integration of AI platforms with enterprise systems.
• Enterprise network integration: designing connectivity between AI/GPU infrastructure and existing environments.
• Hybrid & multi-cloud connectivity: integrating on-premises AI platforms with cloud AI services.
• Service provider & third-party integration: experience architecting connections into external or service-provider-hosted environments.
• Secure exposure of AI services: working knowledge of API gateways, service mesh, and mutual TLS.
• Cross-functional design: proven ability to partner directly with network and security architects.
• Working knowledge of model risk management frameworks and responsible AI principles.
• Familiarity with data privacy regulation as applied to AI training and inference data.
• Working knowledge of emerging AI-specific regulation and standards.
• Experience establishing model documentation, audit trail, and approval-gate processes for production AI systems.

Security & Cloud

• AI-specific security fundamentals: model security, prompt-injection defenses, supply chain security for open-source/open-weight models.
• Solutions-architect level expertise in at least one hyperscaler, including their native AI/ML services.
• Ability to design for hybrid on-premises/cloud AI deployments.

Architecture Governance & Design Authority

• Proven experience hosting and chairing formal Architecture Review Board (ARB) and Technical Design Authority (TDA) forums.
• Ability to define and operate governance gates across the engagement lifecycle.
• Experience producing and maintaining architecture decision records, design standards, and reference architectures.
• Comfortable presenting and defending architecture decisions to senior client governance bodies.

Communication & Delivery

• Excellent executive communication and presentation skills.
• Proven ability to author low-level design (LLD) and high-level design (HLD) documentation.
• Experience running technical design workshops with senior client stakeholders and multi-vendor delivery teams.

Certifications

• NVIDIA certifications — strongly preferred.
• Cloud AI/ML certification — at least one preferred.
• Kubernetes: CKA or CKAD — preferred.
• TOGAF 9/10 or equivalent enterprise architecture certification — beneficial.

Adjacent Technical Skills (Beneficial, Not Required)

• Networking and data centre design.
• Storage architecture.
• Cybersecurity architecture, particularly zero trust and data protection.
• Traditional enterprise application and integration architecture.
• Software development background for tooling and automation.
• Virtualization/private cloud platforms.

Leadership & Delivery Expectations

• Leads technical delivery independently with minimal oversight.
• Mentors junior and mid-level architects and engineers.
• Builds credibility quickly with highly technical client stakeholders.
• Thrives on ambiguity in a fast-moving technology space.
• Collaborates effectively across sales, pre-sales, delivery, and partner teams.

Education & Experience

• Bachelor's degree in Computer Science, Computer Engineering, or a related technical field, or equivalent demonstrable experience.
• Advanced degree beneficial but not required given sufficient hands-on depth.
• Continuous, demonstrable learning in AI is valued.

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. Applicants to and employees of most private employers, state and local governments, educational institutions, employment agencies and labour organizations are protected under Federal law from discrimination.