Lead Software Engineer - Full Stack - Java, React, AWS

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 Commercial & Investment Banking - Payments 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 the creation of an AI-first Developer Portal experience : define architecture patterns for agent-ready workflows, tool/action schemas, context assembly, and safe execution. Build and operate AI-enabled support experiences (assistant + workflow automation) with contextual understanding of client/workspace state, integration progress, and troubleshooting signals. Design and deliver secure external API platform capabilities : contract-first API design (OpenAPI), versioning strategy, backward compatibility, idempotency, pagination, consistent error models, and developer usability. 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. Own authentication and credential workflows for external integrations , including patterns for certificate handling, credential rotation, and secure onboarding journeys. Establish engineering standards for AI + automation in production : evaluation and test strategy, release gating, telemetry, audit logging, and incident response for AI/tooling systems. Remain hands-on (~50%) through critical-path coding, architecture reviews, code reviews, debugging, and performance/resiliency improvements. Drive cross-functional, federated delivery by partnering with API owners, security, and shared services to deliver a unified client experience despite distributed backend ownership. Coach and develop engineers : set expectations for quality, delivery discipline, design rigor, and collaboration; actively grow technical talent and leadership bench. Required qualifications, capabilities, and skills Formal training or certification in software engineering concepts and 8+ years applied experience; demonstrated coaching/mentoring experience. Hands on software engineering experience building and operating distributed systems at scale. Experience leading software engineering teams and delivering complex platforms/products with measurable outcomes 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 . Strong hands-on backend development experience and hands on experience coding in Java strongly other modern backend stacks acceptable Proven expertise building external-facing APIs and integration platforms , including contract-first design, lifecycle/version management, security controls, and operational readiness. Strong security fundamentals for external integrations: OAuth2/OIDC concepts, mTLS, PKI/certificates, secrets management, secure SDLC, and threat modeling. Experience building AI-enabled product capabilities in production (beyond simple chat): grounded retrieval/search, structured tool/action execution, guardrails, evaluation practices, monitoring, and auditability. Proficient in automation and continuous delivery methods; advanced understanding of agile and modern SDLC practices. Strong understanding of application resiliency and observability (logs/metrics/traces), performance engineering, and incident management. Preferred qualifications, capabilities, and skills Experience working in regulated environments with strong controls, compliance requirements, and reliability expectations. Developer portal or developer-experience platform experience (onboarding journeys, documentation-as-a-product, integration tooling, sandbox/test experiences). Experience building workflow/orchestration systems (state machines, async processing, retries, compensating actions). Search and relevance experience (enterprise search, ranking, contextual retrieval, content quality signals). Cloud-native delivery experience (containers/Kubernetes) and event-driven architectures (webhooks/callbacks/streams). Prior payments, treasury, or broader financial-services platform experience.