Leads end-to-end delivery of secure, scalable Java full-stack applications on AWS. Designs system architectures, APIs, data models, resiliency strategies, and cloud-native services; writes and reviews production code; drives testing, CI/CD, observability, incident response, and operational stability. Provides technical guidance, influences engineering practices, and leads responsible adoption of AI-assisted development tools while ensuring security, correctness, and compliance.
Be an integral part of an agile team that continually pushes the envelope to enhance, build, and deliver high-quality technology products.
As a Lead Software Engineer at JPMorganChase within the Commercial & Investment Banking and Reconciliation team, you will play a critical role in designing, building, and delivering secure, stable, and scalable solutions. You will bring deep expertise in Java full-stack engineering, system design, and software architecture, and you will be expected to drive end-to-end deliveries—from requirements and architecture through build, test, release, and production support.
Job Responsibilities
- Lead end-to-end delivery of complex initiatives: translate business outcomes into technical designs, plan execution, drive delivery milestones, and ensure production readiness.
- Provide technical guidance and direction to business and technical teams, including contractors and vendors.
- Build secure, high-quality production code (Java full stack) and conduct thorough code reviews; debug and uplift code written by others.
- Own system design and architecture decisions: define service boundaries, APIs/contracts, data models, resiliency patterns, scalability strategies, and non-functional requirements (performance, security, availability).
- Drive decisions that influence product design, application functionality, and technical operations/processes (CI/CD, observability, incident response, reliability).
- Serve as a function-wide subject matter expert in one or more focus areas (e.g., Java microservices, cloud-native architecture, distributed systems, AWS).
- Actively contribute to the engineering community as an advocate of firmwide frameworks, tools, and SDLC best practices.
- Influence peers and project decision-makers to adopt leading-edge technologies and modern engineering patterns.
- 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 hands-on experience in Java with strong grasp of backend engineering fundamentals (concurrency, performance, security, testing).
- Proven ability to deliver system design, application development, testing, and operational stability in production environments.
- Demonstrated strength in design and architecture: microservices and distributed systems patterns, event-driven architecture, API design, data consistency, resiliency and fault tolerance.
- Ability to tackle design and functionality problems independently with little to no oversight; comfortable acting as the engineering “driver” to unblock delivery.
- Practical cloud-native experience, including building, deploying, and operating services on cloud platforms.
- Strong ownership mindset with the ability to drive quality across the SDLC: design reviews, implementation, automation, release, and ongoing support.
- 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 in AWS (e.g., designing/deploying cloud-native services, security and IAM concepts, monitoring/observability, cost-aware design).
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