Job DescriptionThe
SVP, AI Deployment & Solutions Leader is responsible for driving the organization's AI deployment, solution architecture, and enablement strategy. This executive leader owns and governs the end-to-end AI lifecycle, ensuring AI investments are translated into scalable, secure, and measurable business outcomes.
A primary responsibility of this role is serving as the enterprise's leading authority on AI platforms, architecture, and deployment strategy. The successful candidate will possess deep expertise in
Microsoft Azure AI services (primary platform) and
AWS AI capabilities, enabling them to advise business and technology leaders on platform selection, technical solution design, architectural patterns, and implementation approaches. This leader will help teams move AI initiatives beyond proof of concept by establishing robust, enterprise-grade architectures that support scalability, reliability, security, governance, and long-term operational success.
The SVP will establish enterprise standards, architectural principles, governance frameworks, and enablement programs that accelerate AI adoption while ensuring alignment with business objectives, risk requirements, and technology strategy. This role partners closely with executive stakeholders across Product, Technology, Operations, Risk, Compliance, and the Business to maximize the value of AI investments across the organization.
Role OverviewIn this role, you will:
- Serve as the organization's senior AI deployment and solutions architecture leader, providing strategic and technical direction on AI platforms, services, models, and implementation approaches.
- Guide business and technology teams in evaluating AI opportunities, selecting the appropriate technologies, and designing scalable AI solutions.
- Establish enterprise architectural standards and reusable design patterns for AI applications, ensuring solutions can scale beyond pilots and proofs of concept.
- Provide expert guidance on Azure AI and AWS AI platforms, including AI Foundry, Azure OpenAI, Azure Machine Learning, Amazon Bedrock, and related services.
- Define and govern a unified enterprise AI technology stack aligned with data, security, governance, and cloud strategies.
- Oversee deployment of highly available, secure, compliant, and resilient AI solutions in production.
- Drive enterprise AI adoption through training, best practices, self-service capabilities, and technical enablement programs.
- Lead and develop a multidisciplinary team of AI architects, ML engineers, platform engineers, data scientists, and technical specialists.
Key ResponsibilitiesEnterprise AI Solutions, Architecture & Technical Advisory for AI Enablement & Acceleration- Serve as the enterprise's primary advisor on AI solution architecture, platform strategy, technical feasibility, and deployment approaches.
- Provide deep technical leadership across Microsoft Azure AI services and AWS AI platforms, helping teams select the most appropriate technologies, models, and architectures to solve business problems.
- Review and recommend scalable architectural designs that enable AI solutions to move from experimentation into enterprise-wide production deployment.
- Establish reusable architecture patterns, reference designs, and implementation standards for Generative AI, Agentic AI, Machine Learning, and Intelligent Automation solutions.
- Provide guidance on solution tradeoffs, platform selection, model selection, integration patterns, security requirements, and operational considerations.
- Evaluate emerging AI technologies and define adoption strategies aligned to business value, risk tolerance, and long-term technology objectives.
- Partner with business and technology leaders to shape AI use cases, solution roadmaps, and implementation strategies.
AI Enablement & Enterprise Feasibility- Lead the enterprise AI feasibility function by providing authoritative guidance on:
- Model selection (traditional ML, Generative AI, Agentic AI)
- Platform selection and cloud architecture
- AI services, tools, frameworks, and orchestration technologies
- Enterprise architecture standards and governance requirements
- Ensure teams leverage enterprise-approved AI platforms and solutions that accelerate delivery and reduce long-term operational risk.
- Identify delivery blockers related to data readiness, platform adoption, infrastructure, governance, or organizational capability and drive resolution.
- Establish and maintain enterprise AI standards, playbooks, best practices, and implementation frameworks.
- Sponsor AI education, technical workshops, architecture reviews, and knowledge-sharing initiatives across the organization.
- Build and lead an AI Center of Excellence focused on innovation, governance, capability development, and talent growth.
AI Production Deployment & Operations- Provide executive oversight of enterprise AI deployment and operationalization strategies.
- Establish standards and governance for AI delivery, including MLOps, CI/CD, model lifecycle management, monitoring, observability, and rollback processes.
- Ensure AI solutions meet enterprise standards for scalability, reliability, security, privacy, regulatory compliance, and resiliency.
- Define operational SLAs, KPIs, and success measures for AI applications and platform services.
- Drive AI operational excellence through governance, monitoring, risk management, and continuous improvement processes.
AI Platform Development- Define the strategy and roadmap for enterprise AI platforms, tooling, APIs, agent frameworks, and self-service capabilities.
- Partner with cloud engineering, infrastructure, DevOps, and security teams to deliver scalable AI platform capabilities.
- Ensure platform investments support the organization's long-term AI strategy, balancing speed of innovation with governance, security, performance, and cost efficiency.
- Champion reusable services, components, and frameworks that accelerate AI delivery across business units.
Leadership & Talent Development- Recruit, develop, and lead a high-performing organization of AI architects, ML engineers, platform engineers, data scientists, and AI specialists.
- Establish talent strategies and capability-building programs that support enterprise AI transformation.
- Foster a culture of innovation, accountability, collaboration, and continuous learning.
- Partner with senior leaders to prioritize AI investments, align resources, and drive measurable business impact.
Preferred Qualifications- Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field, or equivalent experience.
- 12+ years of technology leadership experience, including significant experience leading AI, machine learning, cloud architecture, platform engineering, or related functions.
- Deep expertise with Microsoft Azure AI services, including Azure AI Foundry, Azure OpenAI, Azure Machine Learning, and broader Azure cloud capabilities, with Azure serving as the primary enterprise AI platform.
- Strong knowledge of AWS AI services, including Amazon Bedrock, SageMaker, and related cloud-native AI capabilities.
- Demonstrated ability to evaluate, architect, and deploy AI solutions that successfully scale from proof of concept to enterprise-wide production environments.
- Extensive experience providing technical and architectural guidance on AI platforms, solution design, implementation approaches, and operational readiness.
- Strong understanding of enterprise architecture, cloud-native application development, distributed systems, APIs, data platforms, AI infrastructure, and integration patterns.
- Experience establishing MLOps practices, AI governance frameworks, operational standards, and production support models.
- Experience with agentic AI frameworks and technologies, including LangChain, LangGraph, Azure AI Foundry, Amazon Bedrock, and AI capabilities integrated within enterprise software platforms.
- Proven ability to design scalable, reusable, and secure AI architectures that maximize business value while minimizing operational complexity and technical debt.
- Strong executive presence with demonstrated success influencing senior business and technology leaders.
- Proven people leadership experience leading highly technical, multidisciplinary teams.
Hybrid/Flexible Work Schedule:This role is based in
Minneapolis, MN or
Chicago, IL and requires regular in-office presence to effectively lead enterprise-wide AI initiatives and foster cross-functional collaboration. The position follows a
hybrid work model, with an expectation of
three or more days per week in the office and flexibility to work remotely on remaining days.
Benefits:Our approach to benefits and total rewards considers our team members' whole selves and what may be needed to thrive in and outside work. That's why our benefits are designed to help you and your family boost your health, protect your financial security and give you peace of mind. Our benefits include the following:
- Healthcare (medical, dental, vision)
- Basic term and optional term life insurance
- Short-term and long-term disability
- Pregnancy disability and parental leave
- 401(k) and employer-funded retirement plan
- Paid vacation (from two to five weeks depending on salary grade and tenure)
- Up to 11 paid holiday opportunities
- Adoption assistance
- Sick and Safe Leave accruals of one hour for every 30 worked, up to 80 hours per calendar year unless otherwise provided by law
Review our full benefits available by employment status here.
The salary range reflects figures based on the primary location, which is listed first. The actual range for the role may differ based on the location of the role. In addition to salary, U.S. Bank offers a comprehensive benefits package, including incentive and recognition programs, equity stock purchase 401(k) contribution and pension (all benefits are subject to eligibility requirements). Pay Range: $170,255.00 - $200,300.00
