Job Description Transform your career by enhancing and migrating our trade processing platform to a cloud-native code-base.
As a Software Engineer at JPMorgan Chase within the Commercial & Investment Banking's Markets Technology, you will play a crucial role in an agile team dedicated to enhancing, building, and delivering trusted, market-leading technology products that are secure, stable, and scalable. 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.
Job responsibilities
- Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
- Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
- Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
- Identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
- Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
- Adds to team culture of diversity, opportunity, inclusion, and respect.
- Sets and scales operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams to improve delivery speed, quality, and operational outcomes; establishes measurable expectations (e.g., throughput, defect reduction, reliability) and ensures consistent validation, security, resiliency, and reuse of proven patterns.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive efficiency and support capacity unlock initiatives across teams, prioritizing reuse of existing firm technology assets.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 8+ years applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security. Advanced coding with Java 11 and advanced capabilities with relational databases such as Oracle, PostgresQL, and Mongo DB
- Experience with event-driven development, Kafka and messaging systems such as Java Messaging System (JMS)
- Strong experience developing applications built on microservices architecture while working across a globally distributed team
- Ability to communicate effectively across different organization levels for technical and non-technical audiences, peers, and leadership teams.
- Experience with AWS ecosystem and application migrations from on-premise and hybrid solutions to fully cloud-native applications.
- In-depth knowledge of the financial services industry and their IT systems
- Ability to contribute directly to coding, code reviews, and technical team delivery while partnering with management and stakeholders to ensure delivery is aligned with overall team objectives.
- Experience leading multi-team adoption of enterprise-authorized AI-assisted development and delivery tools, including defining governance/ways of working (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and control expectations; ability to coach managers/leads and influence leaders on safe scaling patterns.