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 Commodities Transformation 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. 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.
Job responsibilities
Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
Develops next-gen applications for Commodities trading desks across the globe at JPMorganChase
Develops secure and high-quality production code, and reviews and debugs code written by others
Liaises with users such as Product and Financial Controllers to understand reporting needs, and investigates and resolves reporting issues
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
Leverages agentic development pipelines to build solutions and accelerate delivery
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 advanced applied experience
Experience working with Front Office Trading Desks
High proficiency in Python
Strong problem-solving skills, with the ability to diagnose and resolve complex reporting and system issues
Proven ability to liaise with business users such as Product and Financial Controllers to elicit reporting requirements and translate them into technical solutions
Solid understanding of Risk and PnL reporting infrastructure paradigms and systems
Hands-on practical experience delivering system design, application development, testing, and operational stability
Advanced in one or more programming language(s)
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 using agentic development pipelines to build and deliver solutions
Ability to tackle design and functionality problems independently with little to no oversight
Practical cloud native experience
Preferred qualifications, capabilities, and skills
Hands-on experience developing on the Athena platform or a comparable Python risk management platform
Domain knowledge of Commodities products and derisking workflows
Experience building or integrating Risk and PnL reporting infrastructure in a Front Office environment
Familiarity with agentic AI development pipelines and their application to production engineering
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security

