Lead Software Engineer: Cloud Infrastructure & Platform Engineering (Java, Python, Agentic AI)

JPMorgan Chase & Co.•New York, NY

4d•$156,750 - $215,000

About The Position

As a Lead Software Engineer (Vice President) at JPMorganChase, within the Consumer and Community Banking you will be part of an agile team enhancing, building, and operating secure, stable, and scalable platform and software solutions for corporate functions including Global Finance, Corporate Treasury, Risk Management, Human Resources, Compliance, Legal, and the Corporate Administrative Office. This role is infrastructure-first (approximately 50% platform/infra engineering), complemented by hands-on Java engineering (approximately 30%) and AI/agentic development (approximately 20%).

Requirements

Formal training or certification on software engineering concepts and 5+ years of applied experience.
Hands-on Java/J2EE and Python development experience, with strong depth in Spring Boot and REST API design
AWS platform/infrastructure engineering experience, including designing and operating cloud workloads
Hands-on experience operating Kubernetes in high-availability environments, including deployments and incident troubleshooting
Strong Infrastructure-as-Code experience with Terraform (modules, state management, secure provisioning practices)
Production support experience using observability/APM tooling (e.g., Splunk, Datadog, Dynatrace), including root-cause analysis and reliability improvements
Experience with testing practices and frameworks (e.g., JUnit, Mockito) and performance testing tools (e.g., JMeter/BlazeMeter or equivalent)
Solid grounding in domain-driven design, microservices patterns, resiliency patterns, and operating services under change/incident management controls
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices

Nice To Haves

Experience with AWS services commonly used in modern platforms (e.g., ELB/ALB, Route 53, API Gateway, Lambda, ECS, managed caching such as Redis)
Experience implementing policy-as-code and guardrails for cloud/Kubernetes environments
Experience building platform “golden paths” (templates, paved roads, reusable modules) that accelerate application teams safely
Experience with secrets management tooling and patterns (e.g., rotation, least privilege, auditability) in regulated environments
Practical experience building or integrating AI/agentic workflows with measurable evaluation, safety controls, and monitoring in production-like environments
Operate Kubernetes platforms (preferably Amazon EKS), including cluster operations, upgrades, deployment strategies, networking/ingress, configuration and secrets management, and troubleshooting in high-availability environments

Responsibilities

Lead the design, build, and day-to-day operation of cloud platform capabilities and production environments at scale, emphasizing resiliency, security, and reliability
Build and maintain secure Java (Java 17+) and Spring Boot services, including RESTful APIs, with strong code review and debugging
Implement Infrastructure-as-Code / Everything-as-Code using Terraform, including secure provisioning and policy controls where applicable
Enable and support safe release practices (e.g., rolling and blue/green deployments) for high-availability, high-scale services
Improve operational stability by eliminating recurring issues and automating remediation/runbooks where appropriate
Apply SRE-aligned practices (SLIs/SLOs, error budgets, incident response) and participate in formal change and incident management processes
Use observability and APM tooling (e.g., Splunk, Datadog, Dynatrace) to monitor, troubleshoot, and improve production services
Deliver CI/CD in a “you build it, you own it” culture using Git and enterprise pipelines (e.g., Jules, Spinnaker, or similar)
Prototype and deliver AI-enabled/agentic workflows (e.g., tool-using agents, orchestration patterns, evaluation and guardrails) and translate stakeholder needs into technical designs
Mentor engineers and partner with application teams to drive best practices across reliability, performance, risk, cybersecurity, and legal/compliance requirements