Lead AI Platform Engineer

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 Summary The Lead Engineer (Generative AI) is a senior technical role responsible for designing, developing, and operationalizing enterprise-scale Generative AI (GenAI) solutions. This position combines deep hands-on expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI architectures with strong technical leadership to deliver secure, scalable, and resilient AI systems. The role partners across engineering, product, and business teams to translate complex requirements into production-ready AI capabilities aligned with enterprise standards for security, risk, and responsible AI.

1. GenAI Solution Engineering Design, develop, and deploy GenAI solutions leveraging: Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) architectures Prompt engineering techniques 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 continuously improve solution quality and innovation 2. GenAIOps & Lifecycle Management Lead the end-to-end lifecycle of GenAI solutions, including: Solution architecture and engineering Integration with enterprise systems Secure deployment and release management Monitoring, observability, and continuous optimization Implement GenAIOps best practices to ensure scalability, reliability, and cost efficiency Establish logging, evaluation, and feedback mechanisms for production AI systems 3. Cloud, Platform & Scalability Engineering Architect and deploy GenAI applications across cloud environments (Azure and AWS) Design distributed systems capable of supporting high-throughput, low-latency AI workloads Leverage modern infrastructure practices: Containerization (Docker) Orchestration (Kubernetes) Infrastructure as Code (Terraform, ARM/Bicep) Ensure high availability, performance, and enterprise-grade security 4. Software Engineering & Architecture 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 supporting automated testing, deployment, and release management Enforce engineering best practices including code reviews, testing, and documentation 5. Technical Leadership & Influence Provide architectural leadership and guidance across GenAI initiatives Drive critical 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 Basic Qualifications Bachelor’s degree, or equivalent work experience  Six to eight years of relevant experience Experience Should Include Bachelor’s or Master’s degree in Computer Science, Engineering, or related field 8+ years of experience in software engineering, platform engineering, or AI/ML solutions 2+ years hands-on experience with GenAI technologies, including LLMs and RAG architectures and vector databases Strong knowledge of agentic AI concepts and frameworks (e.g., LangChain, LangGraph) Experience with cloud platforms (Azure and/or AWS) Deep understanding of distributed systems and scalable architecture patterns