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—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.