Lead Artificial Intelligence Engineer – Development, Infrastructure

U.S. Bank

Lead AI Engineer building Generative AI, RAG, and agentic systems for U.S. Bank’s financial services technology.

Posted 8/6/2026full-timeHopkins • Illinois, Minnesota • 🇺🇸 United StatesSenior💰 $133,365 - $156,900 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and integrating Generative AI solutions, including Retrieval-Augmented Generation (RAG) architectures and vector databases. Proficient in Python and Java, with a strong understanding of software engineering principles, compliance standards, and collaborative practices across cross-functional teams.

Highest-signal resume keywords

Generative AI Solutions DevelopmentPython and Java ProgrammingContainerization with Docker and KubernetesVector Database IntegrationAgile Software Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software DevelopmentGenerative AIRAG ArchitecturesVector DatabasesAPI DevelopmentData StructuresAlgorithmsAI Evaluation PracticesCode ReviewProblem-Solving

Soft Skills

CollaborationCommunicationAnalytical MindsetLearning MindsetCustomer Experience Focus

Tools & Technologies

DockerKubernetesFAISSPineconeWeaviateOpenSearchAzure AI SearchLangChainLangGraphNeo4j

Industry Keywords

AI SystemsData PrivacySecurity StandardsComplianceAgile MethodologiesDevOps PracticesAgentic AIKnowledge GraphGraph DatabasesU.S. Bank Policies

Tech Stack

Tools & technologies

AzureDockerJavaKubernetesNeo4jPythonReact

About the role

Key responsibilities & impact

Design, develop, test, operate, and maintain software and AI-enabled products
Develop production-ready, testable code for assigned components, services, and AI workflows
Build and integrate Generative AI solutions, including Retrieval-Augmented Generation (RAG) pipelines using vector databases
Develop and enhance agentic AI systems that plan, reason, retrieve information, and invoke tools under defined guardrails
Follow architectural patterns and best practices for scalability, reliability, performance, and cost
Troubleshoot model/output quality issues and conduct root-cause analysis for software and AI components
Make implementation decisions with customer and employee experience in mind
Incorporate code review feedback and meet engineering, security, and compliance standards
Participate in code reviews as author and reviewer
Apply compliance, risk, data privacy, security, SRE, and AI evaluation practices
Explore emerging GenAI, agentic framework, and vector search technologies through ideas, prototypes, and proofs of concept
Communicate progress, blockers, and risks while delivering incremental features
Collaborate with engineering, product, data, business teams, vendors, and stakeholders

Requirements

What you’ll need

Bachelor’s degree in computer science, Engineering, or related field, or equivalent practical experience
Six to eight years of relevant software engineering experience
10+ years of overall software development experience in Python and Java or other object-oriented languages
3+ years leading software projects with enterprise-level solutions
2–3 years of containerization and orchestration experience with Docker and Kubernetes
Experience with Bedrock, LLMs, and vector databases
2–3 years of hands-on experience with Generative AI use cases, including RAG architectures, prompt engineering, and evaluation approaches
Experience building and integrating vector databases such as FAISS, Pinecone, Weaviate, OpenSearch, or Azure AI Search
Exposure to agentic AI concepts including multi-step reasoning, tool invocation, workflow orchestration, and AI agents
Practical experience with LangChain and LangGraph, including complex agent workflows and RAG pipelines
2–3 years of experience building data-driven APIs and services using Python
Working knowledge of Agile software development lifecycle and DevOps practices
Understanding of AI-powered features’ impact on workflows, decision-making, and trust
Growing understanding of responsible AI principles, model limitations, and guardrails in regulated environments
Ability to collaborate across engineering, product, data, and business teams
Technical proficiency defining and implementing solution requirements for end users
Ability to communicate processes, design decisions, and results to technical and business stakeholders
Solid understanding of algorithms, data structures, architectural design patterns, and best practices
Strong analytical, problem-solving, and learning mindset
Familiarity with Knowledge Graph concepts and graph databases such as Neo4j or TigerGraph is a plus
Familiarity with modern UI frameworks such as React is a plus
Ability to work from a U.S. Bank location three or more days per week
Applicants must comply with U.S. Bank policies, procedures, Code of Ethics, workplace conduct, and safety policies

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
Pension
Disability accommodations during the application or hiring process