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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
We are seeking a highly skilled AI Data Innovation Engineer to join the Data Innovation and Tools Rationalization team within the Enterprise Data Office. This senior individual contributor will bring hands-on experience building and operationalizing AI-enabled data products, agents, and reusable enablement patterns across modern enterprise data platforms. The role focuses on accelerating AI readiness, establishing safe and scalable patterns, and guiding teams through proven approaches for agent development, semantic grounding, governance, and production operations rather than relying on repeated trial-and-error learning.
About the Data Innovation and Tools Rationalization Team
We are the innovation and tooling engine for the Enterprise Data Office, focused on reusable patterns, accelerators, and tool rationalization that reduce friction and speed up delivery and adoption of governed data products.
Vision | Make data products and AI capabilities easier to build, safer to deploy, and faster to adopt across the bank.
Mission | Deliver reusable data product patterns, accelerators, and clear integration pathways that help teams ship data products faster while enabling safer AI adoption and reducing technology sprawl through disciplined tool evaluation and rationalization.
Values | In addition to U.S. Bank core values, we prioritize:
Head high: We build with excellence. Our work is intentional, high quality, and designed to last, so we are always proud of what we deliver and comfortable standing behind it.
Accountability Over Activity: We take end to end accountability, from problem framing through delivery, adoption, and outcomes.
Strategic Intelligence: We think in systems, anticipate downstream impact, and collaborate to win as a pod, not as individuals.
Relentless Craft: We are passionate about the work we do. Our drive comes from curiosity, purpose, and a genuine love of building impactful solutions.
About the Role
The AI Data Innovation Engineer is a senior individual contributor responsible for advancing enterprise AI capabilities from a data and data product standpoint. This role sits at the intersection of governed data products, agent-enabled experiences, AI platform engineering, and responsible innovation. The engineer acts as a force multiplier by building reference implementations, establishing reusable standards, and providing hands-on technical guidance to teams adopting AI across the Enterprise Data Office and broader organization.
Unlike traditional model-development or research-focused roles, this position emphasizes the practical design, validation, and operationalization of AI-enabled data products and agentic solutions. These capabilities are grounded in trusted enterprise data, semantic layers, business ontologies, metadata, reusable interfaces, and appropriate controls so they can be safely integrated and scaled across Snowflake, Databricks, Microsoft, and cloud ecosystems.
The ideal candidate has already delivered AI-enabled solutions beyond isolated proofs of concept and can help other teams move from experimentation to repeatable delivery. The candidate combines technical depth in modern data and AI platforms with practical experience in agent orchestration, semantic grounding, guardrails, evaluation, observability, deployment, and enterprise operating models.
Key Activities
Key Responsibilities Include:
- Prototype, validate, and operationalize AI capabilities that leverage governed enterprise data products, standardized semantic models, business ontologies, metadata, shared feature representations, and reusable data interfaces.
- Develop and evolve reusable AI enablement patterns including Snowpark workloads, Cortex AI functions, retrieval-augmented generation, tool-enabled agents, agent orchestration, and human-in-the-loop workflows aligned with enterprise platform standards.
- Design and validate conversational data and agent-enabled experiences using capabilities such as Snowflake Cortex Agents and Databricks Genie Spaces, Genie agents, and Genie-powered applications, along with comparable enterprise AI platforms.
- Translate successful experiments into reference architectures, reusable components, implementation playbooks, and adoption guidance that enable teams to deliver safely without relearning foundational patterns for each use case.
- Partner with data governance, risk, security, and control teams to define and implement responsible AI guardrails, policy enforcement, access controls, evaluation criteria, auditability, and human oversight appropriate to each use case.
- Collaborate closely with the AI Center of Excellence to integrate validated AI patterns into enterprise AI experiences.
- Evaluate emerging AI tools, frameworks, protocols, and platform capabilities through targeted proofs of concept and comparative assessments, focusing on interoperability, security, maintainability, enterprise fit, and avoidance of unnecessary technology sprawl.
- Design scalable approaches for semantic grounding and context management, including semantic layers, business ontologies, retrieval strategies, metadata-driven architectures, and knowledge representation patterns.
- Establish AI observability and evaluation practices covering agent traces, prompt and response quality, grounding, tool execution, latency, cost, reliability, safety signals, usage, and operational performance.
- Define reusable patterns for agent identity, authorization, tool access, secrets, policy enforcement, and cross-platform integration.
- Work closely with data engineers, analytics engineers, architects, and data product owners to align AI solutions with enterprise data strategy and platform standards.
- Continuously refine AI assets using evaluation results, telemetry, user feedback, adoption data, and evolving enterprise requirements.
- Serve as a hands-on technical mentor and trusted advisor, using demonstrated delivery experience and technical credibility to guide teams, review approaches, share proven patterns, and reduce duplicated experimentation.
- Facilitate technical reviews and communities of practice that transfer knowledge, improve consistency, and accelerate responsible adoption across teams.
This role requires strong communication, collaboration, and technical leadership skills, along with the ability to work effectively with stakeholders across data, technology, governance, risk, security, architecture, and product teams. The successful candidate will combine hands-on engineering with practical enablement, helping teams make sound architectural choices and move AI-enabled data products from concept to governed, observable, and supportable production use.
Core Competencies:
Knowledge:
- Strong understanding of enterprise data products and how semantic layers, business ontologies, metadata, feature representations, and reusable interfaces enable trustworthy analytics, conversational data experiences, and AI agents.
- Strong understanding of modern AI enablement concepts including retrieval-augmented generation, grounding, prompt and context orchestration, tool calling, agentic workflows, memory patterns, human-in-the-loop controls, and model integration grounded in governed data assets.
- Understanding of enterprise agent ecosystems and interoperability patterns, including when to use platform-native capabilities, APIs, event-driven integration, agent orchestration frameworks, and emerging protocols such as Model Context Protocol (MCP).
- Ability to assess tradeoffs across AI platforms, orchestration patterns, frameworks, and deployment approaches, balancing innovation with scalability, security, governance, maintainability, cost, and strategic platform alignment.
- Strong analytical and problem‑solving skills, with the ability to work effectively in ambiguous or emerging problem spaces.
- Comfortable operating as a senior individual contributor who mentors and enables others, influences outcomes through technical credibility, and creates clarity in ambiguous or emerging problem spaces.
- Strong communicator able to engage effectively with data engineers, platform teams, governance partners, and AI practitioners.
Technical Competence:
- Hands-on experience with modern data and AI platforms such as Snowflake and Databricks, including agent-enabled and conversational capabilities that use governed data products as trusted inputs.
- Experience developing AI‑enabled solutions using Python and SQL, including prototyping, validation, and integration with enterprise data assets.
- Hands-on experience with Snowpark, Cortex AI functions, Cortex Agents, Databricks Genie Spaces, Genie agents, Genie-powered applications, or comparable data-native AI capabilities, with an emphasis on reusable patterns and enterprise adoption.
- Experience implementing semantic retrieval, grounding, semantic-layer, ontology, metadata, or knowledge-representation patterns that connect AI capabilities to governed enterprise data products.
- Experience implementing AI guardrails, evaluations, observability, security, governance, and operational controls for production or production-oriented AI solutions.
- Familiarity with agent integration and orchestration approaches using APIs, event-driven patterns, MCP, and frameworks such as LangGraph, LangChain, Semantic Kernel, or equivalent technologies.
- Experience using AI evaluation, tracing, and observability capabilities such as LangSmith or equivalent platforms, and documenting quality, limitations, costs, risks, and operational performance.
- Experience with CI/CD, automated testing, and deployment patterns for AI workloads using enterprise delivery platforms such as Harness, GitHub Actions, Azure DevOps, or equivalent tooling.
Basic Qualifications
- Bachelor's degree in a related field, or equivalent work experience
- Eight or more years of statistical and/or data analytics experience
Preferred Skills/Experience
- 7-10 years of experience across AI engineering or enablement, data engineering, analytics engineering, platform engineering, data products, or related roles, including demonstrated experience guiding technical adoption across teams.
- Demonstrated experience influencing adoption of shared AI platforms, reusable patterns, tools, or standards in a large enterprise environment.
- Demonstrated hands-on experience building, deploying, or operationalizing AI-enabled solutions or agents beyond isolated proofs of concept.
- Experience designing AI capabilities built on governed enterprise data products, semantic models, business ontologies, metadata, and shared interfaces.
- Experience developing reusable AI enablement patterns such as Cortex AI, Databricks Genie, retrieval-augmented generation, agent orchestration, tool integration, or human-in-the-loop workflows.
- Experience establishing reference architectures, technical standards, implementation playbooks, and mentoring practices that help multiple teams adopt AI safely and efficiently.
- Proven track record of designing reusable components, guardrails, integration patterns, or standards adopted by multiple teams.
- Experience working across Snowflake, Databricks, and cloud ecosystems (Azure, AWS, or GCP).
- Experience working in regulated or large-scale enterprise environments preferred.
- Strong organizational and technical leadership skills with the ability to guide multiple initiatives, communicate tradeoffs, and support teams through implementation decisions.
- Deep understanding of banking and financial institution terms.
- Knowledge of banking regulation and requirements for regulatory reporting.
- Strong analytical, problem-solving, facilitation, and stakeholder-management skills, with the ability to translate emerging technology into practical enterprise guidance.
- Hands-on experience with programming languages such as Python and SQL.
- Experience with distributed data processing using Spark or equivalent technologies; Hadoop or Hive experience may be useful when supporting legacy environments.
- Experience designing conversational analytics, decision-support, or AI-enabled data experiences; familiarity with Power BI, Tableau, or equivalent tools is a plus.
- Experience integrating AI-enabled workflows with Microsoft Power Platform, Copilot Studio, or comparable enterprise automation platforms is a plus.
- Experience operationalizing data and AI pipelines with automated testing, monitoring, observability, and production support practices.
- Excellent written and verbal communication skills, including the ability to document technical patterns, mentor practitioners, facilitate architecture discussions, and present recommendations to senior management.
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: $133,365.00 - $156,900.00 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 $133K-$156K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($145K) sits 32% below the category median. Disclosed range: $133K to $156K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
U.S. Bank AI Hiring
U.S. Bank has 9 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Minneapolis, MN, Denver, CO, Irving, TX, US. Compensation range: $109K - $163K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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