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JPMorgan Chase & Co. seeks a Senior Principal eSoftware Engineer to lead and architect multiple products across agile teams, delivering secure, scalable, market-leading technology. Deep expertise in KDB and AI-enabled workflows will drive innovation, performance, and reliability at scale.
You will collaborate with quantitative, algorithmic, and trading teams to align requirements, validate data solutions, and push the boundaries of real-time and historical market data processing.
We're looking for a tech leader ready to take their career to new heights. Join the ranks of top talent at one of the world’s most influential companies.
As a Senior Principal eSoftware Engineer at JPMorganChase, you provide deep engineering expertise and work across agile teams to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Leverage your deep expertise to consistently challenge the status quo, innovate for business impact, lead the strategic development behind new and existing products and technology portfolios, and remain at the forefront of industry trends, best practices, and technological advances.
Architects and governs agentic AI-enabled engineering workflows (using enterprise-authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams.
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 at scale.
Demonstrated experience designing and leading adoption of agentic AI-enabled development practices (using enterprise-authorized tools within the work environment) across teams, including setting standards for human-in-the-loop validation, auditability/traceability of changes, and secure handling of sensitive data.
Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications, data sensitivity, and risk-based governance; ability to influence senior technical leaders on safe scaling patterns and reuse.