AI Data Innovation Engineer

U.S. Bank•Minneapolis, MN

4d•Hybrid

About The Position

U.S. Bank is seeking a senior individual contributor for its Data Innovation and Tools Rationalization team within the Enterprise Data Office. The role advances enterprise AI capabilities from a data and data product standpoint by building and operationalizing AI-enabled data products, agents, reference implementations, and reusable enablement patterns across modern enterprise data platforms. The engineer helps teams move from experimentation to repeatable delivery by applying trusted enterprise data, semantic layers, business ontologies, metadata, reusable interfaces, and appropriate controls. The work emphasizes practical design, validation, and operationalization—not model research—and spans agent orchestration, semantic grounding, guardrails, evaluation, observability, deployment, and enterprise operating models. The role also provides hands-on technical guidance, mentors teams, and helps establish safe, scalable patterns for adoption across the Enterprise Data Office and broader organization. The position requires close collaboration with data engineers, analytics engineers, architects, data product owners, the AI Center of Excellence, and governance, risk, security, control, technology, and product stakeholders. It requires working from a U.S. Bank location three or more days per week. The stated pay range is $133,365 to $156,900, based on the primary location; actual range may vary by location.

Requirements

Bachelor's degree in a related field, or equivalent work experience.
Eight or more years of statistical and/or data analytics experience.
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.
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.
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 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 Harness, GitHub Actions, Azure DevOps, or equivalent tools.
Strong understanding of enterprise data products and how semantic layers, business ontologies, metadata, feature representations, and reusable interfaces enable trustworthy analytics and AI experiences.
Strong understanding of 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 platform-native capabilities, APIs, event-driven integration, agent orchestration frameworks, and emerging protocols such as MCP.
Ability to assess tradeoffs among 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, including the ability to work in ambiguous or emerging problem spaces.
Ability to operate as a senior individual contributor who mentors and enables others, influences outcomes through technical credibility, and creates clarity.
Strong communication skills and ability to engage with data engineers, platform teams, governance partners, and AI practitioners.
Excellent written and verbal communication, including documenting technical patterns, mentoring practitioners, facilitating architecture discussions, and presenting recommendations to senior management.

Nice To Haves

Seven to ten years of experience across AI engineering or enablement, data engineering, analytics engineering, platform engineering, data products, or related roles, including 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 designing reusable components, guardrails, integration patterns, or standards adopted by multiple teams.
Experience working across Snowflake, Databricks, and cloud ecosystems such as Azure, AWS, or GCP.
Experience working in regulated or large-scale enterprise environments.
Strong organizational and technical leadership skills, including guiding multiple initiatives, communicating tradeoffs, and supporting implementation decisions.
Deep understanding of banking and financial institution terms.
Knowledge of banking regulation and requirements for regulatory reporting.
Experience with distributed data processing using Spark or equivalent technologies; Hadoop or Hive experience may be useful for 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.
Strong analytical, problem-solving, facilitation, and stakeholder-management skills, including translating emerging technology into practical enterprise guidance.

Responsibilities

Prototype, validate, and operationalize AI capabilities using 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.
Design and validate conversational data and agent-enabled experiences using Snowflake Cortex Agents, Databricks Genie Spaces, Genie agents, Genie-powered applications, and comparable enterprise AI platforms.
Translate successful experiments into reference architectures, reusable components, implementation playbooks, and adoption guidance.
Partner with governance, risk, security, and control teams to define and implement responsible AI guardrails, policy enforcement, access controls, evaluation criteria, auditability, and human oversight.
Collaborate with the AI Center of Excellence to integrate validated AI patterns into enterprise AI experiences.
Evaluate AI tools, frameworks, protocols, and platform capabilities through proofs of concept and comparative assessments focused on interoperability, security, maintainability, enterprise fit, and avoiding unnecessary technology sprawl.
Design scalable approaches to 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 with data engineers, analytics engineers, architects, and data product owners to align AI solutions with enterprise data strategy and platform standards.
Refine AI assets using evaluation results, telemetry, user feedback, adoption data, and evolving enterprise requirements.
Mentor and advise teams, review technical approaches, share proven patterns, and reduce duplicated experimentation.
Facilitate technical reviews and communities of practice to transfer knowledge, improve consistency, and accelerate responsible adoption across teams.
Communicate and collaborate with stakeholders across data, technology, governance, risk, security, architecture, and product teams.

Benefits

Healthcare (medical, dental, and 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 (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
Incentive and recognition programs
Equity stock purchase
Pension