About The Role

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 Global Banking and Wholesale Loan 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

Lead a feature team and contribute hands-on to design, development, and production support.
Design, develop, test, debug, and deploy microservices and batch/EOD components to deliver sprint-ready increments.
Build resilient integrations (REST/event-driven) with upstream/downstream systems and data stores.
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.
Ensure code quality through standards, automated testing, architectural checks, and code reviews/pairing.
Provide accurate estimates, support story mapping/splitting, and contribute to backlog refinement and prioritization.
Facilitate sprint ceremonies (planning, reviews/demos, retros) and communicate progress, risks, and trade-offs.
Drive best practices across engineering (CI/CD, observability, performance, reliability, secure coding).
Mentor and coach engineers, guiding solution design and raising team capability.

Required qualifications, capabilities, and skills

Formal training or certification on software engineering concepts and 5+ years applied experience .
Experience in building and supporting production software with Python and Java (Java 17+).
Strong experience with Spring Boot, REST APIs, and microservices design patterns.
Solid CS fundamentals: OOAD, collections, and concurrency/multithreading with strong SQL skills (complex query development, troubleshooting, and performance considerations).
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
Proven end-to-end ownership: design → build → test → release → operate/support.
Demonstrated technical leadership: code reviews, mentoring, setting standards, and leading delivery while remaining a strong IC contributor.
Clear communication and stakeholder management; ability to translate complex concepts into pragmatic decisions.
Strong quality mindset: testing strategy, CI/CD, reliability, and observability in day-to-day engineering.
Experience working in Agile teams and translating business requirements into implementable designs.

Preferred qualifications, capabilities, and skills

Financial services experience, ideally in commercial lending / loan servicing domains.
Familiarity with commercial credit/loan products and lifecycle processes (servicing, EOD/batch, integrations).
Experience with ACBS (or similar loan servicing platforms).
Experience with Kafka or equivalent event streaming/data distribution technologies.
Strong DevOps practices: CI/CD automation, deployment pipelines, monitoring/alerting, and incident support.