Lead Infrastructure Engineer - GenAI/AI Automation/AWS

JPMorganChase•Wilmington, NC

About The Position

As a Lead Infrastructure Engineer within JPMorganChase’s Consumer and Community Banking team, you will be a key member of a high-performing team delivering infrastructure and performance excellence. You will apply deep knowledge of software, applications, and technical processes in infrastructure engineering, while continuing to develop technical and cross-functional knowledge beyond your primary area of expertise. The role focuses on integrating GenAI-powered agents into the application and infrastructure lifecycle to improve cloud cost optimization and operational efficiency. You will lead FinOps-related GenAI applications and processes, build automation and governance solutions, advise teams on cost-effective decisions, and communicate cost optimization results to stakeholders. You will also use enterprise-authorized AI capabilities to support infrastructure analysis and documentation, validating outputs and handling operational data in accordance with security and sensitivity requirements.

Requirements

Formal training or certification in infrastructure engineering concepts and at least 5 years of applied experience.
Strong understanding of cloud infrastructure and multiple cloud technologies, including experience with public cloud cost management and optimization tools.
Infrastructure engineering technical depth across areas such as hardware, networking, databases, storage, deployment, integration, automation, scaling, resilience, and performance assessment.
Proven experience using GenAI and agentic AI to automate and develop enterprise software delivery and cost optimization solutions.
Hands-on experience implementing FinOps and GenAI agents to optimize AWS cloud costs, with deep understanding of available optimization levers.
Strong problem-solving skills and ability to execute creative, GenAI-driven solutions to technical challenges.
Strong communication and collaboration skills, with demonstrated ability to develop technical and cross-functional knowledge outside the product domain.
Strong understanding of the FinOps Foundation Framework, including its capabilities, lifecycle, and domains.
Proficiency in modern scripting languages and infrastructure as code practices.
Advanced understanding of AWS services for traditional compute, containerization, and serverless architectures.
Demonstrated experience using enterprise-authorized AI capabilities for infrastructure engineering workflows, with strong validation habits and awareness of data sensitivity.
Ability to validate AI-assisted recommendations before implementation, escalate when uncertain, and ensure outcomes align with resiliency, security, and auditability expectations.

Nice To Haves

AWS Engineering or Architect certifications.
FinOps Practitioner or FinOps Professional certification.
Experience leading automation projects using GenAI and agentic AI.
Advanced certifications in infrastructure engineering, cloud technologies, or automation.
Exceptional verbal and written communication skills.

Responsibilities

Lead the development and integration of GenAI-powered agents across application and infrastructure lifecycle phases to automate cost optimization and efficiency improvements.
Discover, analyze, and prioritize cloud cost optimization opportunities using GenAI models and intelligent agents; calculate potential savings using data repositories, reporting, and ETL tools.
Collaborate with teams to design, implement, and scale GenAI agent-based cost optimization recommendations and best practices.
Architect and develop automation runbooks and Policy as Code initiatives that use GenAI agents to improve cloud cost efficiency and strengthen cost governance.
Guide end users and development teams toward cost-effective architectural, operational, and performance decisions.
Communicate FinOps principles and GenAI agent capabilities to stakeholders and leadership; identify waste and maintain dashboards showing cost savings.
Own FinOps-specific GenAI applications and agent processes, ensuring compliance and current documentation.
Use enterprise-authorized AI capabilities to accelerate infrastructure analysis and design documentation, validating outputs and handling operational data according to sensitivity and security requirements.
Drive a workstream or project involving infrastructure engineering technologies and collaborate across platforms to architect and implement changes that resolve issues and modernize technology processes.
Consider upstream and downstream data, systems, and technical implications, and advise on mitigation actions.
Apply reuse-first, AI-assisted practices to identify recurring issues and validate remediation options, ensuring changes are traceable, auditable, resilient, and secure.
Review and validate AI-assisted recommendations before implementation, escalating uncertainty and ensuring outcomes meet resiliency, security, and auditability expectations.