Lead Software Engineer-Go / Kubernetes / AWS

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorgan Chase within the Public Cloud Foundational Services, Cloud Retail Service Enablement team, you will be a key member of an agile engineering team responsible for designing, building, and delivering secure, scalable, and highly available cloud platform solutions. In this role, you will leverage deep technical expertise and strong problem-solving skills to drive meaningful business impact while addressing complex challenges across cloud infrastructure, Kubernetes platforms, and distributed systems.

Our team develops the Kubernetes-native foundational services that empower application teams across the firm to provision, manage, and operate cloud infrastructure safely, efficiently, and in compliance with enterprise standards. You will play a critical role in building the platform capabilities, automation frameworks, and guardrails that enable developers to innovate at scale while maintaining security, reliability, and operational excellence.

Job Responsibilities

• Own and enhance Kubernetes Custom Resource Definitions (CRDs) and Go-based controllers that manage the complete lifecycle of platform capabilities, from request and provisioning through reconciliation and safe decommissioning across multiple clusters and cloud accounts.
• Design, implement, and strengthen admission controls, including validating and mutating webhooks, to enforce platform standards such as immutable provenance, tenant isolation, namespace integrity, and dependency-aware deletion safeguards.
• Develop and maintain secure cross-cluster communication and provisioning mechanisms, including resource proxies, Kubernetes RBAC, identity federation, STS integrations, and least-privilege access models using IRSA and Amazon EKS Pod Identity.
• Advance developer-facing platform experiences by building and improving Terraform providers, Feature Gateway APIs, authentication and authorization frameworks, context-aware routing, and composition engines that translate declarative intent into managed cloud resources.
• Lead deployment and rollout strategies for multi-region and multi-cluster environments, including environment promotion, phased production releases, dependency-aware orchestration, and Infrastructure-as-Code (IaC) and GitOps automation.
• Drive engineering excellence through scalable design patterns, idempotent reconciliation, automated testing, CI/CD reliability, security and threat-model reviews, architecture decision records (ADRs), and adoption of shared frameworks, tooling, and software development best practices.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Strong hands-on experience with Kubernetes, including Custom Resource Definitions (CRDs), operators/controllers, reconciliation patterns, admission webhooks, and RBAC.
• Proficiency in Go (Golang), with experience building and supporting production-grade services, controllers, or platform components.
• Strong knowledge of AWS cloud services, including Amazon EKS, IAM, STS, VPC networking, and workload identity solutions such as IRSA and EKS Pod Identity.
• Solid understanding of Kubernetes and cloud networking concepts, including Services, EndpointSlices, Ingress controllers, Application and Network Load Balancers (ALB/NLB), Network Policies, CNI plugins, and troubleshooting cross-node or cross-cluster network communication.
• Proven experience designing, building, and operating highly available, production-scale platform services and CI/CD pipelines.
• Hands-on experience with automated testing frameworks and methodologies, including integration, regression, performance, end-to-end, and smoke testing.
• Demonstrated ability to work independently in a fast-paced environment with strong organizational, analytical, and problem-solving skills. A high level of attention to detail, ownership, and accountability is essential.
• 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

Preferred qualifications, capabilities, and skills

• Experience designing and operating multi-tenant Kubernetes platforms, including namespace isolation, virtual clusters (vCluster), and least-privilege identity and access models. Hands-on experience with Terraform provider development is strongly preferred.
• Experience working in financial services or other highly regulated industries, with a strong understanding of audit, risk, compliance, security, and control requirements.
• Strong customer-centric mindset with the ability to understand how platform and engineering decisions impact developer experience, operational efficiency, and business outcomes.
• Experience incorporating security and threat modeling into system design is highly desirable.
• Experience using approved AI-enabled development tools and copilots responsibly, applying critical thinking and engineering judgment to validate code quality, correctness, security, maintainability, and performance.
• Familiarity with platform engineering, internal developer platforms (IDPs), GitOps practices, and Infrastructure-as-Code (IaC) frameworks.