Principal Software Engineer - Executive Director

If you are looking for a game-changing career, working for one of the world's leading financial institutions, you’ve come to the right place.

JP Morgan Chase’s ambitions for AI/ML are transformational, but safety and security are non negotiable. As a Principal Software Engineer within AI Governance Engineering, you will design and build platforms and shared services that enable teams to develop, deploy, and operate AI/ML solutions with compliance and governance seamlessly integrated. Your focus will be to deliver best in class developer tooling that reduces friction and operational toil while raising the firmwide standard for security, reliability, auditability, and responsible AI.

This role is suited to a Senior Engineer who can operate as a technical leader across multiple teams—defining architecture, influencing standards, and delivering high impact software that scales.

Job Responsibilities

• Architect and build governance-by-design platforms that embed policy controls, approvals, and evidence collection into the AI/ML delivery lifecycle (from development to validation to deployment and monitoring)

• Develop reusable services and APIs that enable consistent governance capabilities across diverse AI/ML use cases (e.g., model onboarding, registration, metadata capture, lineage, and lifecycle state management)

• Create developer-first tooling (CLI, SDKs, libraries, templates, automated checks) that makes compliant workflows the easiest workflows

• Integrate controls into CI/CD pipelines for ML systems, including automated validation gates, artifact integrity checks, and promotion workflows with audit trails

• Design secure-by-default patterns for sensitive data handling and model operations, collaborating with security partners to meet firm standards

• Implement observability and monitoring for governance platforms, including operational telemetry, usage analytics, and control effectiveness measurement

• Drive engineering excellence through design reviews, reference architectures, coding standards, performance benchmarking, and reliability engineering practices

• Partner with stakeholders across engineering, model risk, compliance, legal, privacy, and business teams to translate governance requirements into scalable technical solutions

• Mentor and grow engineers, providing technical guidance and fostering a culture of quality, ownership, and continuous improvement

• Sets direction and governance for agentic AI-enabled engineering and SDLC/TLM automation within a technical area to drive measurable improvements in speed, quality, and operational outcomes (e.g., AI-orchestrated delivery workflows, release readiness controls, automated test modernization, and incident triage acceleration), while establishing guardrails for validation, security, resiliency, traceability, 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 and support capacity unlock initiatives at scale

Required Qualifications, Capabilities, and Skills:

• Professional software engineering experience with ownership of complex, production-grade systems

• Proficiency in programming languages such as Python, Java, C++, Go, or Scala

• Experience leading adoption of agentic AI-enabled engineering practices (using enterprise-authorized tools within the work environment) across teams, including defining operating expectations (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs
• Strong understanding of responsible AI use and control expectations in engineering workflows, including data sensitivity, resiliency/security implications, and governance; ability to influence leaders on safe scaling patterns and reuse

• Experience designing and operating distributed systems or internal platforms used by multiple teams

• Strong technical communication and stakeholder management skills

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