Senior Principal AI Solutions Architect for Enterprise Cloud

Ingram Micro Inc in

Irvine (CA)

On-site

USD 159,000 - 270,000

Full time

6 days ago

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Job summary

Ingram Micro Inc. seeks a Senior Principal Services Architect to lead end-to-end AI service offerings on Google Cloud, delivering production-grade agents and multi-agent solutions across global customers.

You will define reference architectures, establish standards, and drive reusable assets and playbooks, partnering with business leaders to set priorities and delivery roadmaps. The role emphasizes hands-on architectural leadership, field credibility, and delivering secure, scalable AI

Qualifications

Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field (or equivalent practical experience).
10+ years of experience in software engineering, cloud architecture, solutions architecture, or technical consulting.
5+ years of experience designing and delivering enterprise cloud, AI, machine learning, or data platform solutions.
Experience engaging directly with enterprise customers and executive stakeholders.
Experience leading technical architecture decisions for large-scale implementations.

Responsibilities

Run the Center of Excellence engagement model: own intake, prioritization, and engagement criteria for the team and the regional and partner teams that adopt it, including which scenarios the team takes on, which it declines, and what a completed engagement must produce.
Lead customer-zero and lighthouse engagements, embedded with customers for a matter of weeks, with defined handoff and enablement milestones.
Lead solution architecture and key technical decisions across the practice, and design novel architectures for problems with no established pattern, validating them in real customer environments.
Define the templates, components, tooling, and build patterns the team and partners use, and engineer production agents with customers on them so every solution is built the same way.
Build production agents on the Google Cloud frontier stack and select appropriate models from the Vertex AI Model Garden for each workload.
Implement grounding and retrieval with Vertex AI Search & Conversation, integrate tools through Vertex AI Extensions, Model Context Protocol, and connectors, and build memory, tool use, and multi-step workflows.
Author the security, data-boundary, and performance standards every solution must meet; run quality gates and technical review across engagements, handle escalations, harden agents for production, and refine patterns based on field results.
Deploy the same solution repeatedly across customers through standard pipelines, and publish and govern agents in Google Workspace, Gemini for Google Workspace, Google Chat, and Vertex AI Agent Builder with managed service accounts and identity federation.
Own the library of reusable technical assets: reference architectures, design standards, best-practice patterns, governance standards, playbooks, agents, workflows, and accelerators, built once and reused across customers.
Develop reusable AI offerings and lead packaging of mature agents into marketplace-ready, transactable offers on Google Cloud Marketplace.
Turn proven engagements into certification curriculum and enablement content with Partner Enablement, and co-deliver with system integrator partners until they can deliver independently
Adapt the Google Cloud and AWS forward-deployed and Hypervelocity Engineering playbooks to the midmarket, bringing a multi-vendor perspective to every engagement
Track emerging Google Cloud technology and model-ecosystem capabilities and competitive moves, and decide which the team integrates into its offers and platforms
Mentor engineers, help set technical direction for partner delivery teams, and co-own the AI services roadmap