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# Lead Cybersecurity Architect

- **Company:** JPMorgan Chase & Co.
- **Location:** Jersey City, NJ, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Backup Devices, Cyber Security, Disaster Recovery, Distributed Systems, Machine Learning, OpenAI, Systems Integration, Data Processing, Google Cloud, Retrieval-Augmented Generation, System Availability, Large Language Models, Model Validation, AI Coding Agents, AI Platforms, Amazon Bedrock, Data Pipelines, Serverless Computing
- **Published:** November 5, 2026
- **Apply:** https://jpmc.fa.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1001/requisitions/preview/210795626

## About the Role

* Significant experience designing, delivering, and operating enterprise-scale platforms across on-prem and cloud environments.

 * Strong grounding in AI/ML engineering and the realities of production AI (model lifecycle, data dependencies, reliability, performance, monitoring, drift, and change management).

 * Hands-on understanding of GenAI/LLM systems and modern patterns (e.g., RAG, agents, prompt/model evaluation, safety controls) and how these workloads behave in production.

 * Demonstrated strength in resiliency, availability, recovery, and data protection controls-especially for distributed systems and cloud-native services.

 * Experience leveraging cloud AI and platform services (e.g., AWS, Microsoft Azure, Google Cloud) and integrating managed services into governed enterprise environments.

 * Ability to operate as a strategic partner and influencer, driving alignment across engineering, product, risk, and control stakeholders.

 * Excellent communication skills, with the ability to translate between architecture, risk/control requirements, and engineering execution-producing clear decisions, trade-offs, and documentation.

Preferred Qualifications:

 * Experience translating business objectives and governance/risk requirements into pragmatic engineering standards, reference architectures, and "paved road" enablement patterns.

 * Experience with Amazon Bedrock or equivalent enterprise model access platforms (e.g., Azure OpenAI, Vertex AI), including identity, access control, and governance considerations.

 * Experience with GenAI mediation patterns (e.g., MCP or comparable governed tool/service routing), including auditability, policy enforcement, and controlled connectivity to internal/external services.

 * Experience with Temporal or comparable workflow orchestration platforms for durable automation, long-running AI operations, and agent/workflow reliability.

 * Experience with sandboxed execution, coding agents, secure agent runtimes, or developer workspaces for controlled code execution and experimentation.

 * Experience with token/cost telemetry, usage attribution, chargeback/showback, or FinOps data pipelines for AI platform economics and capacity management.

 * Experience supporting regulated, sensitive, or client-confidential workloads, including strong practices around data handling, privacy, and operational risk.

## Description

As a Lead Architect (VP), you will provide independent oversight while partnering closely with engineering, product, and control stakeholders to embed resiliency-by-design into AI systems and delivery processes. You'll help ensure that AI-enabled solutions-including modern GenAI/LLM workloads and MCP-based service integrations-meet enterprise security, resiliency, and regulatory expectations without slowing innovation., * Lead resiliency-by-design for AI platforms: Define, standardize, and drive patterns for high availability, disaster recovery, backup/restore, capacity planning, and operational readiness for AI/ML and GenAI services.

 * Partner across the firm: Collaborate with Lines of Business and platform/service teams to influence architecture, controls, and operating models that support AI value realization and enablement at scale.

 * Enable responsible scaling of GenAI: Support teams in productionizing LLM-powered capabilities (e.g., retrieval-augmented generation, agentic workflows, evaluation/monitoring, safety guardrails) with reliability, traceability, and appropriate controls.

 * Establish and assess standards: Ensure AI solutions align to enterprise security, resiliency, and external regulatory expectations; identify gaps, define actionable remediation, and drive closure through accountable owners and timelines.

 * Operationalize oversight: Review solution designs, validate runbooks and SLOs/SLAs, and guide teams through readiness checkpoints (testing, failover exercises, incident learnings, and continuous improvement).

 * Drive pragmatic outcomes: Balance governance and speed by creating reusable guardrails, reference architectures, and enablement guidance that engineering teams can adopt consistently across environments.