Lead Cybersecurity Architect - AI/ML Specialty

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—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. 

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