Senior Lead Software Engineer

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 within the Commercial and Investment Bank Payments 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.

Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors

Develops secure and high-quality production code, and reviews and debugs code written by others

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

Serves as a function-wide subject matter expert in one or more areas of focus

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

Experience delivering secure, production-quality code in Python or Java.

Strong foundations in distributed systems, microservices, and platform architecture/design principles.

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 cloud native experience

Preferred qualifications, capabilities, and skills

Understanding of the core concepts of statistical models (regressions, xgboost etc) and applications of those

Understanding of core GenAI concepts and howto apply those in reliability settings

Understanding of agentic systems and how they work in SRE environments to solve reliability problems

Understanding in common ML tools and frameworks such Python, Spark, Air flow and cloud platforms

Understanding in Cloud platforms (preferably AWS) and certifications