Enterprise Architect - AI

Job Description

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.

Adjacent Technical Skills (Beneficial, Not Required)
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, Open Shift/Open Stack), given increasing convergence with AI infrastructure.

Leadership & Delivery Expectations 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.

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.

Job Requirements Enterprise Architect — Artificial Intelligence AI/ML Infrastructure, Platform & Governance Practice Area Enterprise Architecture — AI & Emerging Technology Reports ToManager, ES&ALevel

Enterprise Architect Location Remote  / Client site as required

Travel

Up to 30%, dependent on engagement portfolio

Role Summary 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 s 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…