Full Stack Lead Software Engineer- Python

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 Lead Software Engineer at JPMorganChase within the Data Management, Data Tech 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. 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 creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problemsLead with an AI-driven approach in daily tasksSpearhead the design and development of real-time, mission-critical using PythonTackle large-scale engineering challenges with technologies like Python StackInnovate, troubleshoot, and optimize for performance and stabilityInspire and foster a culture of creativity, inclusion, and technical excellenceLeads 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 architectureLeads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologiesAdds to team culture of diversity, opportunity, inclusion, and respectDrives 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 teamApplies 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 5+ years of relevant software engineering experienceHands-on practical experience delivering system design, application development, testing, and operational stabilityThis role ensures that large language models (LLMs) including models such as Claude, ChatGPT, and comparable enterprise-approved models - are used as controlled, well-understood components of the software engineering lifecycle.Proficiency in automation and continuous delivery methodsProficient in all aspects of the Software Development Life CycleAdvanced understanding of agile methodologies such as CI/CD, Application Resiliency, and SecurityDemonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)In-depth knowledge of the financial services industry and their IT systemsPractical cloud native experienceDemonstrated 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 securityStrong 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: Expertise in GenAI usage, transforming Software Engineering with an AI-first approachAdvanced proficiency in Python software engineering skillsProficiency in Typescript, RAG, Vector, Graph, data structures, AWS and performance tuningAdvanced understanding of CI/CD, application resiliency, and security for AI applications.Proficiency in testing and debugging low latency applications