Lead AI Platform Engineer

US Bank; Chicago (IL); On-site; USD 150,000 - 210,000; Full time

Job summary

U.S. Bank is seeking a Lead Engineer (Generative AI) to design, develop, and operationalize enterprise-scale GenAI solutions, combining hands-on LLM and RAG expertise with strong technical leadership.

You will partner across engineering, product, and risk functions to deliver secure, scalable AI capabilities with a focus on security, reliability, and governance.

Qualifications

• Bachelor's degree or equivalent work experience.
• 6–8 years of relevant experience in software/platform engineering.
• 8+ years of experience in software engineering, platform engineering, or AI/ML solutions.
• 2+ years hands-on experience with GenAI technologies, including LLMs, 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.
• Proficiency in Python and microservices-based development.

Responsibilities

• Design, develop, and deploy GenAI solutions leveraging: Large Language Models (LLMs)

Skills

GenAI architectures

LLMs

Python

Distributed systems

Microservices

Mentoring

Architectural leadership

Education

Bachelor's degree in Computer Science

Master's degree in CS/Engineering

Tools

LangChain

LangGraph

Azure

AWS

Docker

Kubernetes

Terraform

Job description

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.

Key Responsibilities

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
• Proficiency in Python and microservices-based development
• Experience with Docker, Kubernetes, and Infrastructure as Code tools
• Demonstrated technical leadership and mentoring experience

Preferred Qualifications

• 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

Core Competencies

• Technical Depth & Innovation
• Strong expertise in GenAI architectures and evolving AI technologies
• Ability to balance experimentation with enterprise-grade reliability
• Architecture & Systems Thinking
• Designs scalable, distributed, and resilient systems
• Aligns architecture decisions with enterprise standards and long-termin strategy
• Execution & Operational Excellence
• Drives end-to-end delivery from concept through production
• Ensures high