Security & Privileged Access, Java, Linux Lead Software Engineer – Security & Privileged Access

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-Security and Privileged Access at JPMorgan Chase within Corporate Technology's Identity and Access Management 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 problems - 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 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 - Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies - 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. - Adds to team culture of diversity, opportunity, inclusion, and respect - 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 applied experience - Hands-on practical experience delivering system design, application development, testing, and operational stability - Advanced in Java and Shell Scripting programming language(s). Strong design and Object Oriented Programming (OOP) and Design Patterns - Worked on Linux Based systems. Demonstrate proficiency in automation and continuous delivery methods - Experience in authentication, authorization, encryption and role and attribute based access controls (RBAC and ABAC) - Apply advanced understanding of agile methodologies, including CI/CD, application resiliency, and security and exhibit expertise across all aspects of the Software Development Life Cycle - Utilize practical experience with cloud-native technologies and real-time messaging - Work with SQL and NoSQL databases within technical disciplines such as cloud, artificial intelligence, machine learning, or mobile - 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 - In-depth knowledge of the financial services industry and their IT systems - Applied practical knowledge of cloud-native platforms and solutions. - Terraform and Infrastructure as a code - Infrastructure knowledge (OS, Network, Storage), SRE skills (monitoring and observability) - Python