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

Ingram Micro; Irvine (CA); In loco; USD 180.000 - 240.000; Tempo pieno

Descrizione del lavoro

Ingram Micro Inc. seeks a hands-on Senior Principal Services Architect to design, build, test, and deploy AI-powered business solutions across global markets.

You will define reference architectures, standards, and reusable components using Google Cloud services to deliver production-grade agents and integrations. The role partners with business and regional leaders, leads solution architecture decisions, and accelerates delivery with reusable assets and proven engineering practices.

Competenze

• Bachelor's degree in CS/Engineering/IS or equivalent practical experience.
• 10+ years of experience in software, cloud architecture, solutions architecture, or technical consulting.
• 5+ years designing and delivering enterprise cloud, AI, machine learning, or data platform solutions.
• Experience engaging with enterprise customers and executive stakeholders.
• Experience leading technical architecture decisions for large-scale implementations.

Mansioni

• Run the Center of Excellence engagement model: own intake, prioritization, and engagement criteria for teams and regional/partner units.
• Lead customer-zero and lighthouse engagements with defined milestones and handoffs.
• Guide solution architecture across the practice and design novel architectures validated in production.
• Define templates, components, tooling, and build patterns for consistent delivery.
• Build production agents on the Google Cloud frontier stack with appropriate models.
• Publish and govern agents and reusable assets across customers.
• Mentor engineers and co-own the AI services roadmap.

Conoscenze

Customer engagement

Enterprise-scale experience

Solution architecture

Cloud architecture

Formazione

Bachelor's degree or equivalent

Strumenti

GCP

Vertex AI

Gemini

CI/CD

Descrizione del lavoro

Ingram Micro is the largest IT distributor in the world and one of the largest Google Cloud distribution partners globally. This role designs, builds, tests, and deploys AI-powered business solutions that are adopted at scale across global markets. The role defines the reference architectures, engineering standards, and reusable components that govern how Ingram Micro builds AI solutions, and builds the first production agents, AI applications, and integrations on them using Gemini Enterprise, Gemini App Studio, Vertex AI Agent Builder, Vertex AI Search & Conversation, Vertex AI Model Garden, and related Google Cloud services.

The role partners with business, technical, and regional leaders to set use-case priorities and turn them into secure, production-ready solutions that are deployed and reused across many customers and countries. The Senior Principal Services Architect ships real production software rather than proofs of concept, works end to end from discovery through deployment and handoff, and accelerates delivery through reusable assets and proven engineering practices. The role translates the AI services strategy into operational plans and is accountable for the technical direction of the offer portfolio across regions.

The ideal candidate is a hands-on architect who has shipped production AI solutions on the Google Cloud stack, sets standards other engineers adopt, and works credibly with customers, partners, and Google Cloud technical teams.

YOUR ROLE

• 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

Required 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.

Technical Expertise

• 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
• CI/CD and modern deployment practices

Preferred Qualifications

• Google Cloud Certified Professional Cloud Architect.
• 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.