- 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.
Depth in the specialist area above is mandatory. Experience in the following adjacent domains is a strong plus and will be valued in candidate evaluation, since it enables broader solution ownership across engagements.
- Networking and data center design (routing/switching, fabric architectures).
- Storage architecture (all-flash arrays, software-defined storage, parallel file systems).
- Cybersecurity architecture, particularly zero trust and data protection.
- Traditional enterprise application and integration architecture.
- Software development background (Python, Go, or similar) for tooling and automation.
- Virtualization/private cloud platforms (VMware, OpenShift/OpenStack), given increasing convergence with AI infrastructure.
- Leads technical delivery independently with minimal oversight; comfortable being the final technical authority on an engagement.
- Mentors junior and mid-level architects and engineers, raising the technical bar across the team.
- Builds credibility quickly with highly technical client stakeholders as well as executive sponsors.
- Thrives on ambiguity in a fast-moving technology space; makes sound architectural calls with incomplete information.
- Collaborates effectively across sales, pre-sales, delivery, and partner (NVIDIA, hyperscaler, ISV) teams.
- 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.
Requirements:
Enterprise Architect — Artificial Intelligence
AI/ML Infrastructure, Platform & Governance
Enterprise Architecture — AI & Emerging Technology
Remote / Client site as required
Up to 30%, dependent on engagement portfolio
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 center 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.
- 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 and 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 (e.g. colocation, managed GPU-as-a-service, external inference endpoints).
- 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.
- 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 (not just advisory) on enterprise-scale AI or HPC infrastructure programmes.
- 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 center facilities: power density, liquid cooling, and rack-level design considerations specific to AI compute.
- Storage: parallel and high-throughput file systems (e.g. Everpure, WEKA, VAST, NetApp) sized for training and checkpointing workloads.
- Orchestration & containers: Kubernetes, Docker, Slurm, Run:ai or equivalent GPU scheduling platforms.
- 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 (SageMaker, Azure ML, or Vertex AI).
- Generative AI: LLM fine-tuning (LoRA/QLoRA), RAG architecture design, vector databases (Pinecone, Milvus, Weaviate), and agentic frameworks (LangChain, LangGraph, Semantic Kernel).
- Data pipelines: data lake/lakehouse architectures, ETL/ELT, and data quality/lineage tooling that feed AI systems.
- Infrastructure-as-code: Terraform and Ansible for repeatable, automated provisioning of GPU clusters and AI platform environments; GitOps (ArgoCD) for continuous, declarative platform delivery.
- Pipeline orchestration: Kubeflow Pipelines, Apache Airflow, or Argo Workflows to orchestrate multi-stage training, fine-tuning, and inference pipelines.
- Cluster & workload scheduling: Slurm, Run:ai, and NVIDIA Base Command Manager for GPU job scheduling; Kubernetes-native GPU scheduling including device plugins and MIG partitioning for multi-tenant clusters.
- CI/CD/CT for ML: automated model testing, validation gates, and promotion pipelines (continuous training/continuous delivery) that move models safely from experimentation to production.
- Infrastructure & GPU observability: NVIDIA DCGM, Prometheus/Grafana, and related telemetry stacks for GPU utilization, thermal, and cluster health monitoring.
- Model & LLM observability: production model performance monitoring, data/concept drift detection, and LLM-specific observability (token usage, latency, cost, hallucination/quality metrics) using tools such as Arize, WhyLabs, or Langfuse.
- Logging & tracing: centralized logging (ELK/OpenSearch) and distributed tracing (OpenTelemetry) across data, training, and inference pipelines for end-to-end root-cause analysis.
- Platform integration: API-based and event-driven integration of AI platforms with enterprise systems, using REST/gRPC APIs and message/event streaming platforms (e.g. Kafka).
- Enterprise network integration: designing connectivity between AI/GPU infrastructure and existing campus, data center, and WAN environments, including capacity and latency planning for east-west training traffic and north-south inference traffic.
- Hybrid & multi-cloud connectivity: integrating on-premises AI platforms with cloud AI services via dedicated interconnects (Direct Connect, ExpressRoute) and multi-cloud/hybrid connectivity patterns for distributed training or burst inference.
- Service provider & third-party integration: experience architecting connections into external or service-provider-hosted environments — colocation interconnects, managed GPU-as-a-service offerings, and third-party/external inference endpoints — including the commercial and technical boundary considerations involved.
- Secure exposure of AI services: working knowledge of API gateways, service mesh, and mutual TLS as applied to exposing or consuming AI services safely across organizational and network boundaries.
- Cross-functional design: proven ability to partner directly with network and security architects to define end-to-end integration architecture spanning AI platforms, enterprise networks, and external/customer environments.
- Working knowledge of model risk management frameworks and responsible AI principles (fairness, explainability, human oversight).
- Familiarity with data privacy regulation (GDPR, CCPA) as applied to AI training and inference data.
- Working knowledge of emerging AI-specific regulation and standards: EU AI Act, NIST AI Risk Management Framework, ISO/IEC 42001.
- Experience establishing model documentation, audit trail, and approval-gate processes for production AI systems.
- 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 (AWS, Azure, or GCP), including their native AI/ML services.
- Ability to design for hybrid on-premises/cloud AI deployments, including data residency and sovereignty constraints.
- Proven experience hosting and chairing formal Architecture Review Board (ARB) and Technical Design Authority (TDA) forums, including agenda ownership, decision logging, and stakeholder facilitation.
- Ability to define and operate governance gates across the engagement lifecycle: design authority sign-off, change control, and exception/waiver management for AI platform decisions.
- Experience producing and maintaining architecture decision records (ADRs), design standards, and reference architectures that are actively enforced through ARB/TDA governance.
- Comfortable presenting and defending architecture decisions to senior client governance bodies, including constructively challenging non-compliant or high-risk designs.
- Excellent executive communication and presentation skills; able to flex between deep technical detail and C-suite framing.
- Proven ability to author low-level design (LLD) and high-level design (HLD) documentation to a professional services standard.
- Experience running technical design workshops with senior client stakeholders and multi-vendor delivery teams.
