As we continue to accelerate the transformation of our technology and services, we are placing security and compliance at the heart of our engineering and platforms. This is an opportunity to lead at the intersection of infrastructure engineering, identity security, and AI—shaping how teams across the firm build and govern technology at scale.
As a Principal Software Engineer at JPMorganChase within Infrastructure Platforms Engineering & AI Practices, you will help design and build platforms and shared services that meet our access, compliance, and governance requirements so that we are always secure from the start. Your focus will be to collaborate with teams across our organization to ensure that our platforms and applications define and leverage patterns and solutions consistently to meet evolving requirements for infrastructure identity and security requirements in an AI-enabled environment.
This role is suited to a senior engineer who has hands‑on skills to lead across multiple teams—defining architecture, engineering practices and standards, and delivering high-impact software that scales.
Job responsibilities
- Architect and build governance-by-design patterns and libraries that embed policy controls, approvals, and evidence collection
- Create standards and tooling (CLI, SDKs, libraries, templates, automated checks) that make it easy to adopt and evidence compliant identity solutions across diverse platforms and services
- Partner with platform owners to ensure that continuous compliance reporting is embedded in the systems we build and consume
- Integrate AI capabilities so that our standard patterns include agent‑ready identity constructs and can leverage AI where useful for reporting and review
- Drive engineering excellence through design reviews, reference architectures, coding standards, performance benchmarking, and reliability engineering practices as part of existing governance processes
- Coach and grow engineers, providing technical guidance and fostering a culture of quality, ownership, and continuous improvement
- Work closely with identity architects, platform engineers, and risk and control partners to ensure we leverage best‑in‑class solutions and embed them in our development practices and tools
- Architects and governs agentic AI‑enabled engineering workflows (using enterprise‑authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI‑driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root‑cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams
- 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 at scale
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and expert applied experience
- Professional software engineering experience with ownership of complex, production‑grade systems in large enterprise environments
- Proficiency in programming languages such as Python, Java, C++, Go, or Scala
- Experience using AI assistants in software engineering including agentic capabilities
- Experience developing engineering practices across teams, including identity and security engineering patterns and standards
- Understanding of responsible AI use and control expectations in engineering workflows, including data sensitivity, resiliency/security implications, and governance
- Experience influencing leaders and engineers across large organizations to drive pattern adoption and reuse
- Experience designing and operating distributed systems or internal platforms used by multiple teams
- Strong technical communication and stakeholder management skills
- Demonstrated experience designing and leading adoption of agentic AI‑enabled development practices (using enterprise‑authorized tools within the work environment) across teams, including setting standards for human‑in‑the‑loop validation, auditability/traceability of changes, and secure handling of sensitive data
- Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications, data sensitivity, and risk‑based governance; ability to influence senior technical leaders on safe scaling patterns and reuse
Preferred qualifications, capabilities, and skills
- Experience delivering systems with auditability, traceability, and compliance requirements in regulated environments
- Ability to influence architecture decisions and collaborate effectively across teams
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