Python Senior Lead Software Engineer- Fixed Income Systematic Portfolios

JPMorgan Chase & Co.•New York, NY

2d•$171,000 - $260,000

About The Position

As a Python Senior Lead Software Engineer- Fixed Income Systematic Portfolios at JPMorganChase within the Asset and Wealth Management 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. You will fundamentally change how JPMorganChase Asset Management manages fixed income systematic portfolios, how accounts are constructed, optimized, and scaled day to day. As a lead in Systematic Portfolio Management Technology, you’ll partner with portfolio managers and quants to turn research into production systems that directly drive investment decisions. You’ll build and evolve the platform that powers portfolio optimization and performance insight, from data through to user experience. The team works on agentic flows - designing intelligent, automated workflows that accelerate research-to-production, reduce friction, and raise the quality bar.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience
Hands-on practical experience delivering system design, application development, testing, and operational stability
Advanced experience coding with Python
Ability to tackle design and functionality problems independently with little to no oversight and to tackle design and functionality problems independently with little to no oversight, balancing speed, rigor, and risk
Practical cloud-native experience, including building, deploying, and operating services with strong observability and resilience
Experience with systematic investing or quantitative analytics workflows, including working with market/portfolio datasets and producing performance and risk insights for stakeholders
Familiarity with portfolio optimization concepts and tooling (e.g., commercial optimization solvers), the ability to productionize research prototypes into scalable services, working knowledge of interactive research and prototyping workflows (e.g., notebooks) and performance-focused compute patterns (parallel/asynchronous processing)
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 in Computer Science, Computer Engineering, Mathematics, or a related technical field

Nice To Haves

Experience building systematic portfolio construction/optimization platforms in a front-office environment
Familiarity with optimization solvers and numerical methods (e.g., Gurobi, Axioma, MSCI Open Optimizer)
Strong Python for analytics plus experience productionizing research workflows and experience with Java, Kotlin, Clojure, or other functional languages
Experience integrating market data sources and building reliable analytics pipelines (e.g., Bloomberg/FactSet/MSCI)

Responsibilities

Leads the design and delivery of core systematic portfolio tooling, translating quantitative research and portfolio needs into resilient production platforms.
Partners with portfolio managers and quantitative researchers to build optimization, risk/exposure, and performance insight workflows that are clear and actionable for stakeholders.
Drives agentic automated flows across the lifecycle from data to analytics to execution and controls, raising standards for reliability, scalability, and operational excellence.
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
Influences peers and project decision-makers to consider the use and application of leading-edge technologies