Leads software engineering for JPMorganChase’s Payments Technology team. Responsibilities include designing, developing, testing, reviewing, troubleshooting, and operating secure Java and Spring Boot microservices. The role drives observability, resilience, automation, quality standards, AI-assisted engineering adoption, and production stability while evaluating architectures with vendors and internal teams. Preferred experience includes trade finance, event-driven systems, cloud-native delivery, frontend technologies, security, disaster recovery, and FinOps.
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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
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
- Java and Spring Boot
- Microservice design: service boundaries, API contracts and versioning, idempotency, and resilience (timeouts, retries, circuit breakers).
- Oracle and JPA performance: N+1 queries, indexing, connection pools, transaction boundaries, and Liquibase migration strategy with rollback.
- Observability and production ownership: SLOs, alerting, tracing, and incident and root-cause leadership.
- Testing and quality gates: unit, integration and contract tests, with coverage and Sonar standards. Proficient in all aspects of the Software Development Life Cycle
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Advanced in one or more programming language(s)
- 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
- Trade finance and architecture: Trade finance domain knowledge; event-driven design with Kafka or MQ and the outbox pattern.
- Frontend and data: React/TypeScript, Salt design system, micro-frontends behind the proxy, and modern alternatives to Jasper for reporting and analytics.
- Cloud-native delivery: AWS or private cloud, Terraform/IaC, Docker, Kubernetes/Kustomize, CI/CD, blue-green or canary deployments, and multi-environment configuration.
- Resilience and performance: Load testing, capacity planning, DR and BCP design, and multi-region or active-active setups.
- Security, AI, and cost: OAuth2/OIDC/ADFS, JWT, session management, least privilege, OWASP, secrets handling, CVE/FARM remediation and dependency currency; governed use of Copilot-style tools; FinOps and cost optimisation.
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