Leads development and operation of API gateway, microservices, and platform engineering capabilities on AWS. Designs secure, scalable APIs and event-driven systems; establishes reusable engineering standards; contributes to architecture, coding, reviews, testing, automation, CI/CD, reliability, and production support. Partners across teams and coaches engineers on responsible AI-assisted development, secure validation, and operational excellence.
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As a Lead Software Engineer at JPMorganChase within the Consumer and Community Banking, 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 Consumer and Community Banking, 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
- Design, build, and enhance microservices framework components and platform services used across the Consumer Bank.
- Engineer and operate API gateway and API management capabilities (e.g., Kong) to support external APIs.
- Implement scalable, secure patterns for APIs, microservices, and event-driven applications across cloud and edge platforms (e.g., AWS, Akamai).
- Partner with application teams and platform stakeholders to define standards, reusable patterns, and reference implementations.
- Contribute to architecture, analysis, design, development, code reviews, and technical documentation.
- Drive engineering excellence through automation, testing, reliability practices, and continuous improvement in CI/CD pipelines.
- Participate in Agile delivery and support production readiness through operational ownership and incident follow-up.
- 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
- Advanced knowledge of cloud platforms and infrastructure architecture.
- Strong understanding of containerization and orchestration (Docker, Kubernetes).
- Proficiency in one or more programming languages: Java, Python, and/or Golang (7+ years).
- Hands-on experience with Infrastructure as Code, including Terraform for AWS provisioning (3+ years).
- Experience building and operating API capabilities, including APIs, API gateways, and API management (3+ years).
- Experience deploying and operating workloads on AWS, using EKS (3+ years).
- Strong engineering fundamentals: architecture, analysis, design, coding, and code review. Proficiency with modern developer toolchains and engineering best practices.
- Demonstrated ability to collaborate in large, cross-functional teams and contribute to an inclusive, innovative engineering culture. Experience with Agile, Lean, and Continuous Delivery practices.
- 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
- Deep expertise with AWS services supporting platform engineering, networking, security, and observability.
- Experience with event-driven architectures and managed messaging/streaming patterns.
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