At U.S. Bank, we’re on a journey to do our best. Helping the customers and businesses we serve to make better and smarter financial decisions and enabling the communities we support to grow and succeed. We believe it takes all of us to bring our shared ambition to life, and each person is unique in their potential. A career with U.S. Bank gives you a wide, ever-growing range of opportunities to discover what makes you thrive at every stage of your career. Try new things, learn new skills and discover what you excel at—all from Day One.

Job Description

About the Role

We're hiring an AI/ML Engineer to build and productionize models and AI-powered features that power our fintech products (expenses, travel, fraud/compliance, analytics). You'll own the full lifecycle of AI systems including data pipelines, prompt engineering, RAG/retrieval optimization, agentic orchestration, model evaluation, deployment, and monitoring—with a strong emphasis on reliability, cost-efficiency, and measurable business impact.

Responsibilities

• Own model lifecycle end-to-end: data acquisition, feature engineering, training/eval, online/batch serving, monitoring & retraining.
• Ship production models for ranking/relevance, anomaly detection/fraud, forecasting, and GenAI/RAG-based features with clear SLAs and acceptance criteria.
• Design and orchestrate AI workflows (LangGraph, Airflow, Dagster) with clear handoffs, fallbacks, and observability.
• Build comprehensive evaluation harnesses for LLMs & RAG: offline metrics (RAGAS, retrieval accuracy), in-context learning quality, human preference alignment, and cost-per-query tracking.
• Integrate AI models/agents with product experiences via robust APIs; collaborate with Product/Design/Backend to deliver user-visible impact with transparent fallback patterns.
• Partner with Data Engineering on scalable schemas, multi-tenancy requirements, and performance optimization (freshness, cost, reliability).
• Implement responsible AI governance: PII masking, audit trails, hallucination detection, guardrails, and explainability in regulated contexts.
  
  
  

Basic Qualifications

• Bachelor’s degree, or equivalent work experience
• Two to three years of relevant experience
  
  
  

Preferred Skills/Experience

• Proven experience building and operating production ML/LLM systems, including monitoring, dashboards, and rollback strategies
• Strong LLM and agentic architecture skills: prompt engineering, RAG, tool/function calling, and multi‑step reasoning
• Hands‑on LLM evaluation and testing using RAGAS, semantic similarity metrics, and human preference calibration
• Experience with GenAI orchestration frameworks (LangChain, LangGraph, LlamaIndex) and leading LLM SDKs
• Deep understanding of vector search and retrieval systems, embedding quality, ranking, and cost/latency trade‑offs
• Solid AI security and governance practices: PII‑safe prompting, audit trails, output validation, hallucination mitigation
• AWS‑based deployment experience (SageMaker, Bedrock, Lambda), Docker/Kubernetes, and CI/CD pipelines
• Strong observability mindset: tracking token usage, latency, cost, model drift, and overall AI system health
• Tech Stack: Python/TypeScript, LangGraph, LangChain, Vector Databases, RAG, LLM Evals, AWS SageMaker/Bedrock, Kubernetes, SQL
  
  
  

Location expectations

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

If there’s anything we can do to accommodate a disability during any portion of the application or hiring process, please refer to our disability accommodations for applicants.

Benefits:

Our approach to benefits and total rewards considers our team members’ whole selves and what may be needed to thrive in and outside work. That's why our benefits are designed to help you and your family boost your health, protect your financial security and give you peace of mind.

Posting may be closed earlier due to high volume of applicants.