Interested in this AI/ML Engineer role at U.S. Bank?
Apply Now →Skills & Technologies
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
-------------------
The Chief Architect – AI Platform \& Enterprise Architecture is the senior architecture leader accountable for defining, governing, and evolving the enterprise AI technology architecture that enables scalable, secure, and responsible AI adoption across the organization.
This role sets architectural vision, establishes enterprise standards and guardrails, and drives alignment across platform, data, engineering, security, risk, and product teams. The Chief Architect ensures technology decisions advance business strategy, reduce duplication, improve resilience, and accelerate delivery of production\-ready AI/ML capabilities.
Key Responsibilities
- Define and maintain the enterprise architecture vision, principles, reference architectures, and decision frameworks for AI/ML platforms and adjacent technology domains.
- Establish architecture standards that promote reuse, interoperability, resiliency, security, and speed\-to\-market across products, platforms, and shared services.
- Drive architecture convergence across teams to reduce fragmentation, duplicate capabilities, technical debt, and long\-term operational cost.
- Partner with senior technology and business leaders to ensure architecture roadmaps directly support strategic priorities and measurable business outcomes.
- Own the end\-to\-end architectural direction for enterprise AI/ML platform capabilities, including model integration, inference services, retrieval patterns, orchestration, and automation.
- Define production\-grade patterns for observability, scalability, model/data traceability, policy enforcement, and responsible AI controls.
- Ensure platform capabilities can support secure onboarding of AI/ML use cases across DEV, UAT, and PROD environments with clear guardrails and repeatable patterns.
- Guide platform investment decisions by balancing innovation, risk, cost, operational complexity, and business value.
- Lead enterprise architecture strategy across hybrid and multi\-cloud ecosystems, including Azure, AWS, on\-premises services, data platforms, integration layers, and shared platform capabilities.
- Define architectural patterns for secure data access, service integration, API management, event\-driven flows, workload placement, and cost\-aware scaling.
- Assess technology options and vendor/platform choices, making recommendations that balance enterprise standards, delivery velocity, security posture, and total cost of ownership.
- Ensure solution architectures align to enterprise security, compliance, resiliency, performance, and operational support requirements.
- Lead the architecture community of practice, establishing forums, review mechanisms, and decision processes that improve consistency without slowing delivery.
- Provide senior technical stewardship across domains, resolving complex cross\-platform dependencies and ensuring end\-to\-end solution integrity.
- Coach and mentor architects, senior engineers, and technical leaders to raise architectural maturity and systems\-thinking capability across the organization.
- Represent architecture in executive, risk, compliance, product, and engineering governance forums.
- Define architecture guardrails for security, privacy, compliance, data protection, model governance, auditability, and responsible AI.
- Identify and manage architectural risk across platforms, integrations, dependencies, and production operations.
- Ensure risk decisions are clearly documented, communicated, and aligned with enterprise control expectations.
- Translate complex architecture choices into clear executive\-level recommendations, tradeoffs, risk implications, and investment options.
- Influence senior stakeholders across business, product, technology, security, and operations to align on enterprise architecture priorities and roadmap sequencing.
- Serve as a trusted advisor to technology leadership on platform strategy, modernization, vendor decisions, emerging technologies, and long\-term capability planning.
- Drive alignment between architecture strategy, portfolio funding, delivery execution, and measurable business outcomes.
- Establish architecture\-led operating mechanisms that improve transparency, dependency management, delivery predictability, and production readiness.
- Partner with engineering and platform leaders to ensure architectural decisions are reflected in backlogs, roadmaps, delivery plans, and operational support models.
- Promote disciplined use of Agile practices, Jira hygiene, architecture decision records, structured status reporting, and issue escalation paths.
- Hold teams accountable for solution quality, resiliency, performance, lifecycle ownership, and timely resolution of architectural risks.
Basic Qualifications
- Bachelor's degree, or equivalent work experience
- 10 or more years of relevant software engineering experience
- Six or more years of experience leading multiple software engineering teams
Preferred Skills
- Extensive experience leading enterprise architecture, platform architecture, or large\-scale technology transformation in complex organizations.
- Proven ability to set architectural vision, define standards, govern technical decisions, and influence executive\-level strategy.
- Deep expertise in distributed systems, cloud\-native architecture, data platforms, API/service integration, security architecture, and production operations.
- Strong understanding of AI/ML platform architecture, model integration patterns, responsible AI considerations, and data\-driven systems.
- Demonstrated success leading across engineering, product, data, security, risk, compliance, and operations teams without relying solely on direct authority.
- Excellent executive communication skills, including the ability to explain complex technical tradeoffs, risks, and investment choices clearly.
- Experience establishing or maturing an enterprise architecture function, architecture review board, or architecture community of practice.
- Experience with regulated, data\-sensitive, or highly governed environments.
- Familiarity with architecture frameworks, decision records, technology road mapping, portfolio governance, and technical debt management.
- Experience shaping enterprise AI, GenAI, agentic AI, RAG, or intelligent automation strategies.
- Relevant architecture or cloud certifications such as TOGAF, AWS Certified Solutions Architect, Azure Solutions Architect, or equivalent experience.
Location Expectations
- This role requires working from a U.S. Bank location three (3\) or more days per week
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: $181,730\.00 \- $213,800\.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 $181K-$213K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $250,000. This role's midpoint ($197K) sits 10% below the category median. Disclosed range: $181K to $213K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
U.S. Bank AI Hiring
U.S. Bank has 11 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer. Positions span New York, NY, US, Minneapolis, MN, US, Irving, TX, US. Compensation range: $101K - $252K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.