Lead Cybersecurity Architect

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.

Job Responsibilities:



* 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.

Required qualifications, capabilities, and skills:



* 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...