AI/ML Deployment and Enablement Lead

U.S. Bank National Association • Minneapolis, MN

3d • Hybrid

About The Position

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 Lead 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 Lead 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 Lead 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 compliance.
In 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 platform 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.

Nice To Haves

• Masters degree or equivalent work experience
• Strong hands-on experience delivering AI projects in production, including engineering, deployment, and monitoring of AI use cases
• Deep familiarity with Microsoft Azure AI suite and AWS AI offerings, with Azure as the primary platform
• Exposure to a wide range of AI platforms, tools, and models, including custom modeling and execution
• Experience with AI application delivery and use case development, not just machine learning or data science
• Ability to review and provide technical feasibility guidance for AI requests across the enterprise
• Leadership and people management skills, ideally with experience managing developers and data scientists
• Experience with agentic AI use cases and tools (e.g., LangChain, LangGraph,AI Foundry, Amazon Bedrock, 3rd party agentic capabilities thru vendors in CRM, ERP & other spaces), with deep knowledge to guide what will be more suitable for solving business problems using gen AI models, human agents, and AI agents and suggest strong technical design to promote reuse and scale for value
• Familiarity with enterprise-scale AI enablement, including documentation, education, and self-serve approaches

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, mentorship, and continuous capability development.
• AI Production Deployment
• Provide leadership and oversight for enterprise MLOps practices, including CI/CD, model registry, and automated rollback.
• Ensure AI systems meet regulatory, security, and privacy standards in collaboration with risk and compliance stakeholders.
• Define and review SLAs, KPIs, and observability standards for AI services, ensuring operational excellence and accountability.
• AI Architecture
• Set the architectural vision for a scalable, modular AI ecosystem spanning data ingestion, feature stores, training infrastructure, and inference.
• Champion standards for model governance, including versioning, data lineage, explainability, and auditability.
• Evaluate emerging AI technologies and approaches, and define adoption roadmaps aligned with business value and risk tolerance.
• AI Platform Development
• Own the strategic evolution of the on‑prem AI platform stack (e.g., Airflow, Elasticsearch) and its operating model.
• Ensure delivery of self‑service platforms, APIs, and tooling that enable teams to innovate efficiently and safely.
• Partner with cloud, DevOps, and security leaders to balance performance, cost efficiency, scalability, and compliance.

Benefits

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 501-1,000 employees