AI​/ML Deployment and Enablement Leader

## AI/ML Deployment and Enablement Leader Apply locations:
Minneapolis, MN:
Chicago, ILtime type:
Full time posted on:
Posted Yesterday time left to apply:
End Date:
June 30, 2026 (27 days left to apply) job requisition :
At U.S. Bank, we’re on a journey to do our best. Helping the customers and businesses we serve to make better and smarter financial decisions and enabling the communities we support to grow and succeed.  We believe it takes all of us to bring our shared ambition to life, and each person is unique in their potential. A career with U.S. Bank gives you a wide, ever-growing range of opportunities to discover what makes you thrive at every stage of your career.

Try new things, learn new skills and discover what you excel at—all from Day One.##
** Job Description
** The AI/ML Deployment and Enablement Leader owns and governs the end-to-end AI lifecycle across the organization, setting direction, standards, and execution mechanisms that deliver measurable business impact. This role provides strategic oversight to ensure teams design, build, and operate AI solutions that meet enterprise requirements for scale, reliability, and compliance, including high-availability production deployments. The leader drives enterprise AI enablement and feasibility by guiding model and platform selection, defining a unified AI stack and architectural patterns, and establishing playbooks, training, and best-practice frameworks.

This position also leads a multidisciplinary team and partners closely with product, operations, engineering, risk/compliance, and business leaders to align AI investments with strategic priorities and operational excellence.
** Role Overview
** The Leader of AI/ML Deployment and Enablement will own and govern the end‐to‐end AI lifecycle across the organization. This role is responsible for setting direction, establishing standards, and ensuring execution of AI initiatives that deliver measurable business impact.

You will provide strategic oversight and leadership, ensuring teams design, build, and operate AI solutions that meet enterprise requirements for scale, reliability, and  this role, you’ll:
* Oversee the deployment of high‐availability AI models into production, ensuring reliability, latency, and regulatory compliance.
* Enable and scale adoption of AI platforms, services, agents, and MCPs by establishing training programs, best‐practice frameworks, and enabling tooling.
* Define and govern a unified AI stack aligned with data strategy, security requirements, and long‐term scalability goals.
* Sponsor and guide the development of a self‐service AI platforms that democratizes experimentation, deployment, and monitoring across the organization.

You will lead and develop a multidisciplinary team of data scientists, ML engineers, platform engineers, and domain experts, while partnering closely with product, operations, and business leaders to align AI investments with strategic priorities.
** Key Responsibilities
**** AI Enablement & Feasibility (Enterprise‐Wide)
** Own the
** enterprise AI feasibility function**, providing authoritative guidance on
* Model selection (traditional ML, GenAI, agentic AI)
* Approved platforms, tools, and services
* Architectural patterns and trade‐offs
* Act as the
** primary technical advisor
** to business and technology teams evaluating AI use cases.
* Ensure teams are guided toward
** enterprise‐approved solutions
** that accelerate delivery and reduce long‐term operational risk.
* Identify delivery blockers early (data readiness, platform onboarding, governance dependencies) and drive resolution.
* Establish and maintain AI playbooks, standards, and best‐practice frameworks for internal teams.
* Sponsor and guide enterprise‐wide workshops, training, and knowledge‐sharing initiatives.
* Provide consultative guidance on AI solutions and architecture, partnering with product, engineering, and business teams to shape use cases, design patterns, and implementation approaches.
* Act as a trusted advisor to teams evaluating AI feasibility, trade‐offs, and architectural alignment with enterprise standards.
* Build and lead an AI Center of Excellence to drive talent strategy,…