Senior Principal Services Architect, AI & Agentic Solutions (Google Cloud)
About The Position
Ingram Micro is hiring a Senior Principal Services Architect to define and deliver production AI-powered business solutions using Google Cloud technologies. The role establishes reference architectures, engineering standards, and reusable components for AI solutions, and builds production agents, applications, and integrations using technologies such as Gemini Enterprise, Gemini App Studio, Vertex AI Agent Builder, Vertex AI Search & Conversation, and Vertex AI Model Garden. Working with customers and business, technical, regional, and partner leaders, the architect sets use-case priorities and turns them into secure, production-ready solutions that can be deployed and reused across customers and countries. The role spans discovery through deployment and handoff, leads technical direction across the practice, and translates the AI services strategy into operational plans. It also develops reusable AI offerings, supports partner enablement, and contributes to the AI services roadmap.
Requirements
- 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.
- Technical expertise in Google Cloud Platform, Vertex AI, Gemini, generative AI and large language models, agentic AI architectures, retrieval-augmented generation, AI orchestration frameworks, API integrations and tool calling, Model Context Protocol, cloud security and governance, infrastructure as code, 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
- Own the Center of Excellence engagement model, including intake, prioritization, engagement criteria, and expected deliverables.
- Lead customer-zero and lighthouse engagements, setting handoff and enablement milestones.
- Lead solution architecture and key technical decisions across the practice, including validating novel architectures in customer environments.
- Define reusable templates, components, tooling, and build patterns, and engineer production agents with customers using them.
- Build production agents on the Google Cloud frontier stack and select suitable models from Vertex AI Model Garden.
- Implement grounding and retrieval, integrate tools through Vertex AI Extensions, Model Context Protocol, and connectors, and build memory, tool use, and multi-step workflows.
- Set security, data-boundary, and performance standards; run quality gates and technical reviews; handle escalations; and harden agents for production.
- Deploy solutions repeatedly through 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, patterns, playbooks, agents, workflows, and accelerators.
- Develop reusable AI offerings and package mature agents as 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 Google Cloud and AWS forward-deployed and Hypervelocity Engineering playbooks for the midmarket, bringing a multi-vendor perspective to engagements.
- Track 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
- Certain roles may be eligible for merit increases, annual bonus or sales incentives, and long-term incentives.
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What This Job Offers
Job Type
Full-time
Career Level
Principal
