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 Commercial and Investment Bank 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 delivering critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job Responsibilities
- Execute creative software solutions, design, development, and technical troubleshooting, thinking beyond routine or conventional approaches to build solutions or break down technical problems.
- Deliver end-to-end solutions in the form of cloud-native, microservices-based applications, leveraging the latest technologies and best industry practices.
- Use domain modeling techniques to build best-in-class business products, structuring software for clarity, testability, and evolution.
- Develops secure high-quality production code, and reviews and debugs code written by others.
- 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.
- Continuously update technologies and patterns to keep systems current.
- Design and build solutions that avoid single points of failure using scalable architectural patterns.
- Design and build scalable, secure, and reliable solutions by leveraging modern architectural patterns that ensure zero-downtime releases and optimize data performance.
- 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.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years of hands-on experience in software engineering, including system design, application development, testing, and operational support.
- Proficiency in back-end technologies (e.g., Java, Springboot, Node.js ) with experience building microservices-based applications; for full-stack roles, proficiency also includes front-end technologies (e.g., HTML, CSS, JavaScript, Typescript, React, Angular).
- Demonstrated ability to independently solve design and functionality challenges with minimal supervision.
- Experience working with cloud platforms (e.g., AWS, Azure, GCP), distributed systems, and web technologies, including RESTful APIs and web services, WebSockets, and JSON.
- Hands-on experience designing and building scalable applications using SQL and NoSQL databases. Understanding of application resiliency
- Experience with agile development methodologies (e.g., Scrum) and an understanding of the software development life cycle.
- Proficiency in automation and continuous delivery methods. Proficient in all aspects of the Software Development Life Cycle.
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security.
- Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.).
- 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
Preferred qualifications, capabilities, and skills
- Strong soft skills, including presentation, negotiation, mentoring, and stakeholder management.
- Strong problem-solving, analytical, and communication skills.
- Ability to drive broader impact by sharing and contributing best practices.
- Experience in the banking domain. AWS certification.