Lead AI Platform Engineer

U.S. Bank National Association•Chicago, IL

1d•$139,230 - $163,800•Hybrid

About The Position

The Lead Engineer (Generative AI) is a senior technical role responsible for designing, developing, and operationalizing enterprise-scale Generative AI solutions. The position combines hands-on expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI architectures with technical leadership to deliver secure, scalable, and resilient AI systems. The role partners with engineering, product, and business teams to translate complex requirements into production-ready AI capabilities aligned with enterprise standards for security, risk, and responsible AI. The role leads the design, development, deployment, and full lifecycle management of GenAI solutions, including integration with enterprise systems, secure releases, monitoring, observability, and continuous optimization. It architects cloud-based applications across Azure and AWS and builds scalable Python and microservices applications supported by containerization, Kubernetes, infrastructure as code, and CI/CD practices. The position also provides architectural direction, makes technical design decisions, mentors engineers, and works with product, business, risk, and security stakeholders. This role requires working from a U.S. Bank location three or more days per week. The listed pay range is $139,230 to $163,800. U.S. Bank offers a comprehensive benefits package, subject to eligibility requirements.

Requirements

Bachelor’s degree or equivalent work experience.
Six to eight years of relevant experience (basic qualifications).
Bachelor’s or master’s degree in Computer Science, Engineering, or a related field (experience should include).
Eight or more years of experience in software engineering, platform engineering, or AI/ML solutions.
Two or more years of hands-on experience with GenAI technologies, including LLMs, RAG architectures, and vector databases.
Strong knowledge of agentic AI concepts and frameworks such as LangChain and LangGraph.
Experience with Azure and/or AWS cloud platforms.
Deep understanding of distributed systems and scalable architecture patterns.
Proficiency in Python and microservices-based development.
Experience with Docker, Kubernetes, and infrastructure-as-code tools.
Demonstrated technical leadership and mentoring experience.

Nice To Haves

Experience implementing GenAI solutions in enterprise or regulated environments.
Familiarity with observability frameworks and AI lifecycle tooling.
Understanding of AI governance, security, and compliance requirements.
Experience contributing to or working with AI/ML or GenAI frameworks.
Background in financial services or other highly regulated industries.

Responsibilities

Design, develop, and deploy GenAI solutions using LLMs, RAG architectures, prompt engineering, and agentic AI workflows and orchestration.
Build intelligent systems using frameworks such as LangChain, LangGraph, AWS Bedrock, and Microsoft Foundry Agent Service.
Evaluate emerging tools and frameworks to improve solution quality and innovation.
Lead the end-to-end lifecycle of GenAI solutions, including architecture, engineering, enterprise integration, secure deployment, release management, monitoring, observability, and continuous optimization.
Implement GenAIOps practices to support scalability, reliability, and cost efficiency; establish logging, evaluation, and feedback mechanisms for production AI systems.
Architect and deploy GenAI applications across Azure and AWS cloud environments.
Design distributed systems for high-throughput, low-latency AI workloads.
Use containerization, orchestration, and infrastructure-as-code practices, including Docker, Kubernetes, Terraform, and ARM/Bicep.
Ensure high availability, performance, and enterprise-grade security.
Develop scalable, maintainable applications using Python and microservices-based architectures.
Apply secure coding standards and robust data-handling practices for regulated environments.
Build and manage CI/CD pipelines for automated testing, deployment, and release management.
Enforce engineering practices including code reviews, testing, and documentation.
Provide architectural leadership and guidance across GenAI initiatives and drive design decisions for large-scale, complex AI solutions.
Mentor and coach senior engineers and development teams.
Translate business requirements into scalable, secure, and resilient technical solutions.
Partner with stakeholders across product, business, risk, and security functions.

Benefits

Healthcare coverage, including medical, dental, and vision.
Basic term and optional term life insurance.
Short-term and long-term disability coverage.
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 hours worked, up to 80 hours per calendar year unless otherwise provided by law.
Incentive and recognition programs.
Equity stock purchase program.
Pension, subject to eligibility requirements.