Enterprise Architect - AI

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, OpenShift/OpenStack), 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.