Lead Software Engineer-Kubernetes | Infrastructure As Code | Terraform | Ephemeral Environments

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 JPMorganChase within the Commercial & Investment Bank Payments Technology team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
• Develops secure and high-quality production code, and reviews and debugs code written by others
• 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.
• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
• Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Hands-on professional software engineering experience, including building platforms, automating infrastructure, and developing developer tools
• Strong proficiency in Go, Rust, or Java - writing production-grade, well-tested, maintainable code for platform services operating at scale
• Solid experience with Kubernetes - including deployments, namespaces, services, ingress, ConfigMaps/Secrets, and familiarity with custom controllers or operators
• Hands-on experience with infrastructure-as-code (Terraform, Ansible) - writing modules, managing state, and provisioning infrastructure repeatably across environments
• Experience with at least several of the following data technologies in an automation context: Oracle, PostgreSQL, MongoDB, CockroachDB, Cassandra, Kafka, DynamoDB
• Experience building or extending CI/CD pipelines (Jenkins, Spinnaker, GitHub Actions, or Argo CD) - including custom stages, shared libraries, and webhook integrations
• Understanding of distributed systems fundamentals - networking, service discovery, DNS, load balancing, and the operational challenges of multi-service architectures
• 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

• Environment provisioning systems (namespace-per-PR, composable/virtual environments)
• Service mesh (Istio/Linkerd): traffic routing, header-based matching, traffic splitting
• Test data management: snapshot/restore automation, masking, synthetic data generation
• Developer experience tooling: CLIs, Backstage/dev portals, API docs, self-service workflows
• Container networking/connectivity: cross-cluster, hybrid cloud/on-prem, regulated environment compliance/auditability