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Ingram Micro is a leading technology company for the global information technology ecosystem. With the ability to reach nearly 90% of the global population, we play a vital role in the worldwide IT sales channel, bringing products and services from technology manufacturers and cloud providers to business-to-business technology experts. Our market reach, diverse solutions and services portfolio, and digital platform Ingram Micro Xvantage™ set us apart. Learn more at www.ingrammicro.com

Come join our team where you’ll make technology happen in surprising ways. Let’s shape tomorrow - it’ll be a fun journey!

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.

The ranges above reflect the potential annual base pay across the U.S. for all roles; the applicable base pay range will depend on the candidate’s primary work location, pay grade, and variable compensation plan. Individual base pay within each range depends on various factors, in addition to primary work location, such as complexity and responsibility of role, job duties/requirements, and relevant experience and skills. Base pay ranges are reviewed and typically updated each year. Offers are made within the base pay range applicable at the time of hire. New hires starting base pay generally falls in the bottom half (between the minimum and midpoint) of a pay range.

At Ingram Micro certain roles are eligible for additional rewards, including merit increases, annual bonus or sales incentives and long-term incentives. These awards are allocated based on position level and individual performance. U.S.-based employees have access to healthcare benefits, paid time off, parental leave, a 401(k) plan and company match, short-term and long-term disability coverage, basic life insurance, and wellbeing benefits, among others.

This is not a complete listing of the job duties.  It’s a representation of the things you will be doing, and you may not perform all these duties.

Please be prepared to pass a drug test and successfully pass a pre-employment (post offer) background check.

Ingram Micro Inc. is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, veteran status, or any other protected category under applicable law.