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.