Enterprise Architect - AI

Enterprise Architect — Artificial Intelligence AI/ML Infrastructure, Platform 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.

Required Technical Skills Git Ops (Argo CD) 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 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 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.

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 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