VP AI Lead Infrastructure Engineer

JP Morgan Chase; New York (NY); On-site; USD 140,000 - 210,000; Full time

Job summary

JP Morgan Chase is seeking a Lead Infrastructure Engineer to join its Consumer and Community Banking division in New York. You will apply deep cloud and infrastructure expertise to design, deploy, and optimize complex systems, blending AI-assisted techniques with secure, auditable processes.

In this role you will drive cross-functional initiatives, implement scalable infrastructure, and ensure resiliency and observability across platforms using Kubernetes, Terraform, and Python.

Qualifications

• 5+ years of infrastructure engineering experience with cloud environments.
• Deep knowledge of cloud platforms and networking concepts.
• Hands-on experience running ML/GenAI workloads in production.

Responsibilities

• Apply expertise to projects of moderate scope and deliver design solutions.
• Use AI capabilities to accelerate infrastructure analysis and documentation.
• Drive a workstream and coordinate changes to modernize tech processes.
• Assess data/system implications and advise mitigations.
• Promote AI-assisted, reusable automation with traceability and security.

Tools

AWS

Kubernetes

Terraform

CI/CD

Python

OpenTelemetry

Job description

Assume a vital position as a key member of a high-performing team that delivers infrastructure and performance excellence. Your role will be instrumental in shaping the future at one of the world's largest and most influential companies.

As a Lead Infrastructure Engineer at JPMorganChase within the Consumer and Community banking, you apply deep knowledge of software, applications, and technical processes within the infrastructure engineering discipline. Continue to evolve your technical and cross-functional knowledge outside of your aligned domain of expertise.

Job Responsibilities

• Applies technical expertise and problem-solving methodologies to projects of moderate scope and executes creative solutions for design, development, and technical troubleshooting for problems of moderate complexity
• Uses enterprise-authorized AI capabilities within the work environment to accelerate infrastructure analysis and design documentation, validating outputs and handling operational data according to sensitivity and security requirements.
• Drives a workstream or project consisting of one or more infrastructure engineering technologies and works with other platforms to architect and implement changes required to resolve issues and modernize the organization and technology processes
• Strongly considers upstream and downstream data and systems or technical implications and advises on mitigation actions
• Applies reuse-first, AI-assisted practices within delivery and automation routines to identify recurring issues and validate remediation options, ensuring changes are traceable/auditable and aligned to resiliency and security expectations.

Required qualifications, capabilities, and skills

• Formal training or certification on infrastructure engineering concepts and 5+ years applied experience with deep experience on at least one major cloud provider (AWS preferred: compute, networking, IAM, managed Kubernetes, observability).
• Deep knowledge of one or more areas of infrastructure engineering such as: hardware, networking terminology, databases, storage engineering, deployment practices, integration, automation, scaling, resilience or performance assessments
• Hands-on experience running ML or GenAI workloads in production, such as consuming hosted model APIs (Amazon Bedrock, Azure OpenAI, Google Vertex AI or similar) behind AI gateways, with authentication, rate limiting and resilience.
• Kubernetes experience, including GPU scheduling and auto-scaling. Infrastructure as code with Terraform (or equivalent); CI/CD for infrastructure.
• Strong Python experience to turn manual work into automated solutions. Observability experience with tools such as Splunk, Dynatrace, Datadog, Grafana or OpenTelemetry.
• Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support infrastructure engineering workflows with strong validation habits and awareness of data sensitivity.
• Ability to review and validate AI-assisted recommendations before implementation, escalating when uncertain and ensuring outcomes align to resiliency, security, and auditability expectations.
• Deep knowledge of one specific infrastructure technology and scripting languages
• Deep knowledge of cloud infrastructure and multiple cloud technologies with the ability to operate in and migrate across public and private clouds