Lead Software Engineer-Agentic Identity

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 Consumer & Community Banking, 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

• 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.

• 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
• Adds to team culture of diversity, opportunity, inclusion, and respect

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Advanced in one or more programming language such as Java or Python

• 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

• Demonstrated expertise in identity and access management (IAM), including hands-on experience with OAuth 2.0, OpenID Connect (OIDC), and token-based authorization frameworks; ability to define and enforce identity standards across distributed, multi-agent environments.
• Practical experience securing agentic AI systems, including defining identity, authentication, and authorization models for non-human principals (agents, services, pipelines) operating at enterprise scale.
• Familiarity with emerging agentic integration standards (e.g., Model Context Protocol or equivalent tool/context-exchange protocols) and their security and governance implications in enterprise deployments.
• 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., AWS, cloud, artificial intelligence, machine learning, mobile, etc.)

• Working knowledge of delegated authorization patterns, token exchange flows (e.g., RFC 8693), and least-privilege scope models for automated workflows
•  Exposure to verifiable credential frameworks or decentralized identity concepts as applied to machine-to-machine trust
•  Experience defining lifecycle management policies for non-human identities, including provisioning, rotation, revocation, and audit