Leads architecture and hands-on development of reusable application integration and streaming capabilities. Builds secure production code, APIs, SDKs, shared services, and engineering patterns while guiding product design and technical decisions. Establishes governed AI-assisted engineering workflows, validation controls, automation, testing, observability, and secure coding practices. Partners across product and architecture teams, mentors engineers, and drives cloud-native delivery in complex enterprise environments.
Join a dynamic team where your expertise shapes innovative engineering solutions and drives impactful business outcomes. Advance your career by contributing to cutting-edge technology and collaborative growth.
As a Senior Lead Software Engineer at JPMorgan Chase within the Horizontal Engineering team, you will drive technical leadership and deliver reusable cross-product capabilities for Application Integration & Streaming products. You will own architectural direction, influence product design, and serve as a subject matter expert. You will work hands-on with AI-enabled delivery practices, tackle complex engineering challenges, and foster a collaborative team culture.
Job responsibilities
- Develop secure, high-quality production code for reusable capabilities, shared components, services, APIs, SDKs, and engineering patterns.
- Drive decisions influencing product design, application functionality, and technical operations across horizontal engineering.
- Serve as a subject matter expert in areas such as controls engineering, data management, cloud-native architecture, AI-enabled delivery, or engineering standardization.
- Influence peers and project decision-makers to adopt leading-edge technologies, including AI agents and AI-first delivery practices.
- 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.
- Establish disciplined human-plus-AI workflows, including context curation, prompt management, review checkpoints, approval gates, escalation paths, auditability, and output validation.
- Partner with product teams, architecture, and engineering stakeholders to deliver a governed backlog of cross-product work aligned to strategic goals.
- Tackle design and functionality problems independently, providing technical leadership and mentoring to engineers.
Required qualifications, capabilities and skills
- Formal training or certification on software engineering concepts and 10+ years applied experience
- Advanced proficiency in Java or Python, with strong experience in system design, application development, testing, deployment, automation, CI/CD, infrastructure-as-code, and modern SDLC practices
- Advanced knowledge of software applications and technical processes with in-depth expertise in one or more technical disciplines (cloud, AI, machine learning, mobile, platform engineering, or controls engineering)
- Ability to tackle design and functionality problems independently with little to no oversight
- Strong understanding of cloud-native engineering, application resiliency, secure coding, observability, controls, production support, risk, and operational requirements in complex enterprise environments
- Practical experience using AI-assisted engineering approaches and defining disciplined human-plus-AI workflows, including validation, review checkpoints, auditability, escalation, and quality controls
- 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.
- Experience with React for building modern, responsive front-end applications
Preferred qualifications, capabilities and skills
- Experience with cross-product engineering, reusable platforms, shared services, or engineering standardization across multiple product lines
- Experience with compliance engineering in regulated financial services, data lake and pipelines, and cloud native architecture
- Experience building AI agents, agent-assisted engineering workflows, automated testing, documentation generation, anomaly detection, or developer productivity tooling
- Practical cloud-native experience across private cloud, AWS, Azure, or GCP environments
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