Senior Principal Services Architect, AI & Agentic Solutions (Google Cloud)

Ingram Micro • Irvine, CA

3d • $158,900 - $270,100

About The Position

Ingram Micro is hiring a Senior Principal Services Architect to define and deliver production AI-powered business solutions on Google Cloud that can be adopted across global markets. The architect will establish reference architectures, engineering standards, reusable components, and delivery practices, and will build the first production agents, applications, and integrations using Google Cloud AI services. The role turns AI services strategy into operational plans and is accountable for the technical direction of the offer portfolio across regions.

This is a hands-on, end-to-end role spanning discovery, architecture, engineering, deployment, and handoff. The architect leads customer-zero and lighthouse engagements, works directly with customers and regional and partner teams, and creates repeatable solutions that can be deployed across customers and countries. The role also owns reusable technical assets and helps package mature solutions as marketplace-ready Google Cloud Marketplace offers. The position calls for a practitioner who has shipped production AI solutions, can establish standards adopted by other engineers, and can work credibly with customers, partners, and Google Cloud technical teams.

Requirements

• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent practical experience.
• At least 10 years of experience in software engineering, cloud architecture, solutions architecture, or technical consulting.
• At least 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.
• Technical expertise in Google Cloud Platform (GCP), Vertex AI, Gemini, generative AI and large language models (LLMs), agentic AI architectures, retrieval-augmented generation (RAG), AI orchestration frameworks, API integrations and tool calling, Model Context Protocol (MCP), cloud security and governance, Infrastructure as Code (IaC), enterprise software architecture, and CI/CD and modern deployment practices.

Nice To Haves

• Google Cloud Certified Professional Cloud Architect certification.
• Google Cloud Professional Machine Learning Engineer certification.
• Experience with enterprise AI governance and Responsible AI practices.
• Experience building multi-agent systems and AI copilots.
• Experience deploying AI solutions across multiple customers or business units.
• Experience with AWS and/or Microsoft Azure AI platforms.
• Strong understanding of observability, monitoring, and evaluation frameworks for AI applications.
• Experience creating reusable technical frameworks, accelerators, or service offerings.

Responsibilities

• Run the Center of Excellence engagement model, owning intake, prioritization, and engagement criteria for the team and regional and partner teams, including which scenarios to accept or decline and expected engagement outcomes.
• Lead customer-zero and lighthouse engagements, working with customers for defined periods and establishing handoff and enablement milestones.
• Lead solution architecture and key technical decisions across the practice, including designing and validating novel architectures in real customer environments.
• Define templates, components, tooling, and build patterns, and engineer production agents with customers using those standards.
• Build production agents using Google Cloud AI services and select appropriate models from 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.
• Define security, data-boundary, and performance standards; run quality gates and technical reviews; handle escalations; harden agents for production; and refine patterns using field results.
• Deploy solutions repeatedly across customers using standard pipelines, and publish and govern agents in Google Workspace, Gemini for Google Workspace, Google Chat, and Vertex AI Agent Builder using managed service accounts and identity federation.
• Own reusable technical assets, including reference architectures, design and governance standards, best-practice patterns, playbooks, agents, workflows, and accelerators.
• Develop reusable AI offerings and lead packaging mature agents into transactable offers for Google Cloud Marketplace.
• Turn proven engagements into certification curricula and enablement content with Partner Enablement, and co-deliver with system integrator partners until they can deliver independently.
• Adapt Google Cloud and AWS forward-deployed and Hypervelocity Engineering playbooks for the midmarket and bring a multi-vendor perspective to engagements.
• Track emerging Google Cloud technology, model-ecosystem capabilities, and competitive developments, and decide which to integrate into offers and platforms.
• Mentor engineers, help set technical direction for partner delivery teams, and co-own the AI services roadmap.

Benefits

• Healthcare benefits
• Paid time off
• Parental leave
• 401(k) plan and company match
• Short-term and long-term disability coverage
• Basic life insurance
• Wellbeing benefits
• Some roles may be eligible for merit increases, annual bonuses or sales incentives, and long-term incentives.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior