Full Stack Python / AWS Lead Software Engineer

Job Description We have an opportunity to impact your career and provide an adventure where you can push the limits of what’s possible.

As a Full Stack Python / AWS Lead Software Engineer at JPMorganChase within the Asset and Wealth Management Technology Team- Acceleration Lab, 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; accelerating delivery, removing bottlenecks, and enabling repeatable capabilities in support of the firm’s business objectives.

Job Responsibilities

• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
• Delivers 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
• Adds to team culture of diversity, opportunity, inclusion, and respect
• Promptly investigates and resolves issues, ensuring they do not resurface; Continuously updates technologies and patterns to keep systems current
• Designs and builds solutions that avoid single points of failure using scalable architectural patterns
• 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
• Designs and builds scalable, secure, and reliable solutions by leveraging modern architectural patterns that ensure zero-downtime releases and optimize data performance

Required Qualifications, Capabilities, And Skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Proficiency in back-end technologies (e.g., Python, Flask, Django) with experience building microservices-based applications and proficiency 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
• Experience with agile development methodologies (e.g., Scrum) and an understanding of the software development life cycle
• Understanding of application resiliency
• 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 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…