AI Data Innovation Engineer

U.S. Bank•Minneapolis, MN

6d•$133,365 - $156,900•Hybrid

About The Position

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.

Requirements

Bachelor's degree in a related field, or equivalent work experience
Eight or more years of statistical and/or data analytics experience
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.
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.

Nice To Haves

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.
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.

Responsibilities

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.

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

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