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About This Role
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
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The 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 Overview
In 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 Responsibilities
Enterprise 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.
If there’s anything we can do to accommodate a disability during any portion of the application or hiring process, please refer to our disability accommodations for applicants.
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.
U.S. Bank is an equal opportunity employer. We consider all qualified applicants without regard to race, religion, color, sex, national origin, age, sexual orientation, gender identity, disability or veteran status, and other factors protected under applicable law.
E\-Verify
U.S. Bank participates in the U.S. Department of Homeland Security E\-Verify program in all facilities located in the United States and certain U.S. territories. The E\-Verify program is an Internet\-based employment eligibility verification system operated by the U.S. Citizenship and Immigration Services.
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
U.S. Bank will consider qualified applicants with arrest or conviction records for employment. U.S. Bank conducts background checks consistent with applicable local laws, including the Los Angeles County Fair Chance Ordinance and the California Fair Chance Act as well as the San Francisco Fair Chance Ordinance. U.S. Bank is subject to, and conducts background checks consistent with the requirements of Section 19 of the Federal Deposit Insurance Act (FDIA). In addition, certain positions may also be subject to the requirements of FINRA, NMLS registration, Reg Z, Reg G, OFAC, the NFA, the FCPA, the Bank Secrecy Act, the SAFE Act, and/or federal guidelines applicable to an agreement, such as those related to ethics, safety, or operational procedures.
Applicants must be able to comply with U.S. Bank policies and procedures including the Code of Ethics and Business Conduct and related workplace conduct and safety policies.
Posting may be closed earlier due to high volume of applicants.
Salary Context
This $170K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At U.S. Bank, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. This role's midpoint ($185K) sits 14% below the category median. Disclosed range: $170K to $200K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
U.S. Bank AI Hiring
U.S. Bank has 10 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Chicago, IL, US, Irving, TX, US, Saint Paul, MN, US. Compensation range: $115K - $200K.
Location Context
AI roles in Chicago pay a median of $192,900 across 197 tracked positions. That's 10% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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