Leads backend software engineering within a payments technology team. Designs, develops, tests, troubleshoots, and maintains secure, scalable Java and Spring Boot microservices on AWS. Reviews code, improves automation and operational stability, promotes responsible AI-assisted development, and evaluates architectural solutions with internal teams and vendors. Preferred work includes autonomous agent architectures, multi-agent workflows, LLM integrations, governance, and AgentOps optimization.
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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
Hands-on practical experience delivering system design, application development, testing, and operational stability - Strong hands-on experience building modern applications Java + Spring Boot (backend) with microservices.
- DevOps Fundamentals: Strong working knowledge of Git and CI/CD tools such as Jenkins.
- AWS / Cloud Skills: Hands-on experience with AWS (building/deploying cloud-native services, working knowledge of core services and cloud security basics).
- Strong Communication & Problem Solving: Clear communicator with strong analytical skills, able to collaborate effectively across Product, Architects and QA.
- 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
- Proficient in all aspects of the Software Development Life Cycle
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Preferred qualifications, capabilities, and skills
- System Architecture: Designing and implementing autonomous agent architectures, including cognitive, memory, and planning capabilities.
- Agentic Workflows: Develop end-to-end multi-agent systems where AI agents collaborate to perform tasks such as comprehensive system development, rigorous testing, and thorough validation to ensure reliability and performance. React (frontend)
- Tool Integration: Integrating LLMs with external APIs, databases, and software tools to allow agents to act independently.
- Governance & Safety: Establishing guardrails and security protocols to ensure autonomous actions align with organizational policies.
- Evaluation & Optimization: Debugging, monitoring, and evaluating agent performance (AgentOps) to optimize for accuracy and reliability.
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