Mid-Level AI Software Engineer; Python

U.S. Bank is seeking a Software Engineer who will contribute toward the success of our technology initiatives in our digital transformation journey. This position will be responsible for the analysis, design, testing, development, and maintenance of best‑in‑class software experiences.

This role requires working from a U.S. Bank location three (3) or more days per week.

• Contribute to designing, developing, testing, operating, and maintaining software and AI‑enabled products with guidance from senior engineers.
• Develop production‑ready, testable code for assigned components, services, and AI workflows.
• Assist in building and integrating Generative AI solutions, including Retrieval‑Augmented Generation (RAG) pipelines using vector databases.
• Support the development and enhancement of agentic AI systems, such as task‑oriented AI agents that can plan, reason, retrieve information, and invoke tools under defined guardrails.
• Follow established architectural patterns and best practices, considering scalability, reliability, performance, and cost when implementing AI‑enabled solutions.
• Assist with troubleshooting, model/output quality issues, and root‑cause analysis for traditional software and AI components; propose fixes under guidance.
• Make sound design and implementation decisions with customer and employee experience in mind; elevate risks and questions appropriately.
• Incorporate feedback from code reviews and update implementations to meet engineering, security, and compliance standards.
• Participate in code reviews (as author and reviewer) to learn and apply software and AI engineering best practices.
• Follow compliance, risk, data privacy, and security best practices in all phases of product and AI solution development.
• Learn and apply software reliability engineering (SRE) practices and AI evaluation techniques embedded in the team’s standards.
• Stay curious about emerging technologies in GenAI, agentic frameworks, and vector search, contributing ideas, prototypes, and proofs of concept.
• Contribute to a culture of innovation, collaboration, and continuous improvement.
• Communicate progress, blockers, and risks early; collaborate with the team to deliver well‑scoped, incremental features.

• Bachelor’s degree in computer science, engineering, or related field, or equivalent practical experience.
• 3–5 years of relevant software engineering experience.

• 5+ years of software development experience in Python (Java or other object‑oriented languages is a plus).
• 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 (e.g., FAISS, Pinecone, Weaviate, Open Search, Azure AI Search) for semantic search and AI‑powered retrieval.
• Exposure to agentic AI concepts, such as multi‑step reasoning, tool invocation, workflow orchestration, and AI agents built using modern frameworks.
• Practical experience building AI applications using Lang Chain and Lang Graph, including the design and orchestration of complex agent workflows and retrieval‑augmented generation pipelines.
• 2–3 years of experience building data‑driven APIs and services using Python.
• Exposure to Knowledge Graph concepts and graph databases (e.g., Neo4j, Tiger Graph) is a plus.
• Familiarity with modern UI frameworks (e.g., React) and how AI services integrate into user‑facing applications is a plus.
• Working knowledge of Agile software development lifecycle and Dev Ops practices.
• Understanding of how AI‑powered features impact user 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 to help define and implement solution requirements for end users.
• Ability to communicate processes, design decisions, and results with engineers, product owners, scrum masters, vendors, and stakeholders.
• Solid understanding of algorithms, data structures, architectural design patterns, and best practices.
• Strong analytical,…