Lead Software Engineer - Quantitative Portfolio Construction Development
Confirmed on the employer's own hiring board on Oct 24, 2026. First seen by Alion on Aug 19, 2026. JPMorganChase scores A on the Alion truth index.
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorganChase within the Asset Management Technology, 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. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
JPMorgan Asset Management Technology is seeking a well-rounded hands-on quantitative developer that is experienced in building systems that support the full investment management cycle. Tech stack focus is Java and/or Python. The candidate will be part of the front-office technology team and have good business knowledge and communication skills to work with the stakeholders and develop functional specifications. The candidate will be joining a high-performance, award-winning team who build applications that support the investment management cycle including research, portfolio management and investment.
Job responsibilities
Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
Develops secure and high-quality production code, and reviews and debugs code written by others
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
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.
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
Required qualifications, capabilities, and skills
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 in one or more programming languages (e.g., Python or Java) and experience developing, debugging, and maintaining production code.
Practical cloud native experience.Hands-on experience with Amazon Web Services (AWS) services and cloud delivery practices, including infrastructure as code (e.g., Terraform) and services such as Amazon Simple Storage Service (Amazon S3), Amazon Elastic Compute Cloud (Amazon EC2), and Amazon EMR.
Demonstrated experience leading effective use of approved AI-assisted software development tools (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 engineers on safe, compliant adoption within delivery practices
Proficient in all aspects of the Software Development Life Cycle
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
In-depth knowledge of the financial services industry and their IT systems
Preferred qualifications, capabilities, and skills
Experience with distributed data processing using Spark and modern data platforms (e.g., Databricks).
Proficiency in data engineering and platform development.
Experience with relational databases (e.g., Oracle) and/or cloud data platforms, including performance tuning and query optimization.
Strong analytical skills to support data understanding, quality investigation, and reporting use cases.
Knowledge of banking and financial services domain concepts relevant to data processing and reporting.