Senior Lead Security Architect, AI/ML Platforms

As a Senior Lead Cybersecurity Architect at JPMorganChase within the Cybersecurity and Technology Controls organization, you are an integral part of a team that develops high-quality cybersecurity solutions for AI applications, AI agents, and platform products.

You will drive measurable business impact by applying deep technical expertise and structured problem-solving methodologies to a diverse array of cybersecurity challenges spanning AI, Machine Learning, and agentic systems.

You will partner with product, engineering, and risk stakeholders to identify emerging threats and implement scalable controls that enable responsible innovation.

You will help set technical direction through clear guidance, hands-on design reviews, and measurable risk reduction.

We are looking for an experienced AI Systems Cybersecurity Architect to join our team-not only as an AI/ML security subject matter expert, but as someone who is passionate about advancing safe and secure AI at enterprise scale.

You'll work in a collaborative, trusting, thought-provoking environment that values diversity of thought and creative solutions aligned to our customers' best interests.

Best yet, you will join a team of highly motivated AI and security professionals who will help you build a strong foundation for a long-term career at JPMorganChase.

Job responsibilities


* Develop and enhance security strategies, red teaming programs, and solution designs, while troubleshooting technical issues and creating scalable solutions across AI platforms, AI applications, and agentic workflows.


* Design secure, high-quality AI and software architectures, reviewing and challenging designs and code to ensure adversarial resilience, secure-by-default patterns, and appropriate compensating controls.


* Reduce AI, LLM, and agent security vulnerabilities by applying industry standards and emerging AI safety research, and by evolving policies, testing protocols, and technical controls across the full model development lifecycle (MDLC) and agent runtime.


* Collaborate with stakeholders across product, data science, cyber, legal, and risk to understand AI and agent use cases, drive alignment on AI risk tolerance and mitigation priorities, and recommend modifications during periods of heightened vulnerability, incident response, or regulatory change.


* Conduct discovery, threat modeling, and adversarial testing on generative AI, RAG pipelines, ML systems, and AI agents to identify vulnerabilities such as prompt injection, jailbreaking, data poisoning, tool abuse, insecure memory/context handling, and unauthorized action execution.


* Define and assess agent security/safety controls, including authentication and authorization (authN/authZ) for users, services, and tools; secure session management; least-privilege tool access; and governance for tool/skill registration, enablement, and lifecycle management.


* Provide guidance on secure design, logging, monitoring, and obs...