Lead Artificial Intelligence Engineer – Development, Infrastructure

• 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

• 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, Open Search, or Azure AI Search
• Exposure to agentic AI concepts including multi-step reasoning, tool invocation, workflow orchestration, and AI agents
• Practical experience with Lang Chain and Lang Graph, 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 Dev Ops 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 Tiger Graph 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

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.

• Generative AI Solutions Development
• Python and Java Programming
• Containerization with Docker and Kubernetes
• Vector Database Integration
• Agile Software Development

• Software Development
• Generative AI
• RAG Architectures
• Vector Databases
• API Development
• Data Structures
• Algorithms
• AI Evaluation Practices
• Code Review
• Problem-solving

• Collaboration
• Communication
• Analytical Mindset
• Learning Mindset
• Customer Experience Focus

• AI Systems
• Data Privacy
• Security Standards
• Compliance
• Agile Methodologies
• Dev Ops Practices
• Agentic AI
• Knowledge Graph
• Graph Databases
• U.S. Bank Policies

• Docker
• Kubernetes
• FAISS
• Pinecone
• Weaviate
• Open Search
• Azure AI Search
• Lang Chain
• Lang Graph
• Neo4j