Job description

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

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