AI Data Innovation Engineer

U.S. Bank

. Prototype, validate, and operationalize AI capabilities using governed enterprise data products, semantic models, business ontologies, metadata, feature representations, and reusable data interfaces .

Posted 10/14/2026full-timeUnited StatesSeniorLead💰 $133,365 - $156,900 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and operationalizing AI-enabled solutions using Snowflake, Databricks, and Python, while ensuring compliance with governance and security standards. Proven ability to mentor teams and translate experimental findings into scalable architectures and reusable components.

Highest-signal resume keywords

AI EngineeringSnowflakeDatabricksPythonData Governance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI EnablementData AnalyticsStatistical AnalysisSemantic RetrievalGroundingMetadata ManagementCI/CDAutomated TestingDistributed Data ProcessingOperational Controls

Soft Skills

MentoringCollaborationTechnical Review Facilitation

Tools & Technologies

SnowparkCortex AI FunctionsGenie SpacesGenie AgentsLangSmithAzure DevOpsGitHub ActionsHarness

Industry Keywords

AI GuardrailsData GovernanceCloud EcosystemsEvent-Driven PatternsKnowledge Representation

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPythonSparkSQL

About the role

Key responsibilities & impact

Prototype, validate, and operationalize AI capabilities using governed enterprise data products, semantic models, business ontologies, metadata, feature representations, and reusable data interfaces
Develop 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 platforms
Translate successful experiments into reference architectures, reusable components, implementation playbooks, and adoption guidance
Partner with data governance, risk, security, and control teams to implement responsible AI guardrails, policy enforcement, access controls, evaluations, auditability, and human oversight
Collaborate 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 proofs of concept and comparative assessments
Design scalable semantic grounding and context-management approaches
Establish AI observability and evaluation practices covering agent traces, quality, grounding, tool execution, latency, cost, reliability, safety, usage, and operational performance
Define reusable patterns for agent identity, authorization, tool access, secrets, policy enforcement, and cross-platform integration
Collaborate with data engineers, analytics engineers, architects, and data product owners
Refine AI assets using evaluation results, telemetry, user feedback, adoption data, and enterprise requirements
Mentor and advise teams, review approaches, share proven patterns, and reduce duplicated experimentation
Facilitate technical reviews and communities of practice
Guide AI-enabled data products from concept to governed, observable, and supportable production use

Requirements

What you’ll need

Bachelor's degree in a related field, or equivalent work experience
Eight or more years of statistical and/or data analytics experience
7-10 years of experience across AI engineering or enablement, data engineering, analytics engineering, platform engineering, data products, or related roles
Demonstrated experience building, deploying, or operationalizing AI-enabled solutions or agents beyond isolated proofs of concept
Experience with Snowflake, Databricks, and cloud ecosystems (Azure, AWS, or GCP)
Experience developing AI-enabled solutions using Python and SQL
Hands-on experience with Snowpark, Cortex AI functions, Cortex Agents, Databricks Genie Spaces, Genie agents, Genie-powered applications, or comparable technologies
Experience implementing semantic retrieval, grounding, semantic-layer, ontology, metadata, or knowledge-representation patterns
Experience implementing AI guardrails, evaluations, observability, security, governance, and operational controls
Familiarity with APIs, event-driven patterns, MCP, LangGraph, LangChain, Semantic Kernel, or equivalent technologies
Experience with AI evaluation, tracing, and observability platforms such as LangSmith
Experience with CI/CD, automated testing, and deployment patterns using Harness, GitHub Actions, Azure DevOps, or equivalent tooling
Experience with distributed data processing using Spark or equivalent technologies
Applicants must be able to comply with U.S. Bank policies and procedures, including the Code of Ethics and Business Conduct
Must meet applicable employment eligibility verification requirements

Benefits

Comp & perks

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
Incentive and recognition programs
Equity stock purchase
401(k) contribution and pension