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Sr Lead Software Engineer - Engineering Services & Platforms
senior · Technology / Software Development
Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.
As a Senior Lead Software Engineer at JPMorganChase on the Engineering Services & Platforms (ESP) team within the Chief Technology Office (CTO), 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. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.
The work sits at the intersection of software engineering, enterprise risk, controls, operational resilience, and large-scale technology transformation. As a part of the Engineering Services and Platforms (ESP) team your mission is to… improve SDLC cycle time, delivery consistency, software quality and developer satisfaction by leveraging AI across SDLC tasks, standardizing reusable frameworks, and delivering a unified, end-to-end developer experience across SDLC products, tools and platforms.
Job responsibilities
Provide technical leadership across frontend, backend, API, database, and AI integration workstreams.
Develops secure and high-quality production code, and reviews and debugs code written by others
Build mission-critical technology platforms in complex, highly regulated environments.. Maintain platforms that support critical software delivery and production-release processes across large enterprises.
Build reliable systems and creating engineering ecosystems that are secure, scalable, measurable, auditable, and resilient.
Strengthen TRC frameworks by translating policy and regulatory expectations into practical engineering capabilities.
Modernize SDLC controls, improving the quality and integrity of control evidence, automating risk detection and remediation, strengthening software supply-chain governance, and embedding compliance directly into developer workflows
Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Drives decisions that influence the product design, application functionality, and technical operations and processes
Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Hands-on experience building full stack applications using Java and React (or Angular), with practical experience delivering Spring/Spring Boot microservices and well-designed REST APIs
Proficiency across the full software development lifecycle — requirements, design, implementation, testing, release, and support — with clear documentation and traceability
Hands-on practical experience delivering system design, application development, testing, and operational stability
Experience with Kafka or restAPI Messaging Services
Experience implementing automation and continuous delivery, including build and test automation, CI/CD pipelines, and quality gates
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.
Ability to tackle design and functionality problems independently with little to no oversight
Practical experience developing Production-scale Cloud-native (AWS) engineering solutions in commercial environments
Preferred qualifications, capabilities, and skills
Hands-on container skills with Docker (image builds, runtime config, troubleshooting).
Experience within a Banking and/or Payments environment