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Job Summary
The Enterprise Security Architect will support enterprise security architecture with a focus on reviewing and authoring Architecture Decision Records (ADRs) and Security Architecture Review Board (SARB) submissions. The role combines deep technical expertise across enterprise security with emerging expertise in Generative AI (GenAI) and agentic systems to support secure design, governance, and responsible adoption of intelligent automation. The architect will provide guidance across application, cloud, data, and API security while assessing the security implications of GenAI, LLM, RAG, and autonomous agent architectures.

Key Responsibilities
• Lead security reviews of solution and domain architectures, ADRs, and AI-enabled platforms.
• Assess GenAI and agentic solution designs for model security, data protection, prompt integrity, provenance, and safe agent orchestration.
• Evaluate proposed architectures for alignment with enterprise standards, regulatory expectations, and organizational risk tolerance.
• Produce actionable review comments and traceable recommendations covering traditional and AI-driven architectures.
• Author and maintain ADRs, architectural patterns, and reference architectures, including those covering GenAI system integration, LLM usage, and multi-agent frameworks.
• Ensure architectural documentation clearly communicates problem spaces, options, controls, and technical trade-offs.
• Promote structured architectural reasoning supported by human and GenAI-assisted analysis workflows.
• Define and assess security controls for GenAI systems, including model access, data boundaries, and prompt injection defenses.
• Establish and evaluate guardrails for AI agents performing autonomous actions or multi-step reasoning.
• Assess secure orchestration, isolation, and human oversight mechanisms for agentic systems.
• Evaluate the security of agent frameworks, LLM pipelines, and model-hosting platforms such as Vertex AI and Azure OpenAI.
• Contribute to enterprise policies for responsible AI use and GenAI-assisted development.
• Provide expertise in application, cloud, and data security while incorporating AI security design considerations.
• Support teams in securely embedding GenAI copilots, RAG systems, and autonomous agents within business processes.
• Lead threat modeling for composite systems where GenAI interacts with APIs, data stores, and user environments.
• Use and refine GenAI tools for document review, security design assistance, and ADR quality assurance.
• Develop reusable prompts, review heuristics, and decision frameworks to improve SARB throughput and consistency.
• Mentor peers in human-AI collaborative authoring, emphasizing accountability and verification of AI-generated output.

Required Qualifications
• Bachelor's or Master's degree in Computer Science, Cybersecurity, or a related field.
• 7+ years of experience in architecture or security design, including experience with AI-related systems.
• Demonstrable experience with secure implementation of GenAI or autonomous agents in enterprise environments.
• Strong enterprise security architecture experience across application, cloud, data, and API security domains.
• Experience with GenAI systems architecture, LLM lifecycle, and model governance.
• Experience applying AI security patterns, including threat modeling for LLMs, data leakage prevention, and agent control.
• Strong technical authorship and analytical writing skills, with the ability to clearly articulate architectural decisions and consequences.
• Experience with enterprise security architecture frameworks such as SABSA, TOGAF, and NIST CSF.
• Familiarity with architectural diagramming, review automation, and GenAI-assisted design tools.

Preferred Qualifications
• Experience with technologies and tools such as LangChain, OpenAI GPT, and Guardrails AI.
• Experience evaluating platforms such as Vertex AI and Azure OpenAI.
• Experience supporting enterprise GenAI copilots, RAG systems, and autonomous agents.
• Experience developing reusable architectural prompts, review heuristics, and decision frameworks.

Certifications
• CISSP, CCSP, SABSA, or TOGAF certification preferred.
• AI-specific credentials such as NIST AI RMF, MIT AI Ethics, or Azure AI Engineer preferred.