Enterprise AI Security Architect

Job Overview

• Define & maintain target-state AI security architecture, standard reference patterns, and security/compliance requirements
• Consult on security architecture design reviews for AI systems & solutions within multiple type environments (on-prem, on-cloud and SaaS)
• Collaborate with the security engineering tower to understand existing platform and application security standards owned by SRM, socialize during security reviews and consultation, and help embed security within the teams’ SLDC where possible
• Partner with AI application teams to translate business and technical requirements into secure, approved, and reusable AI architecture patterns.
• Support and enforce approved AITC / ADITRAC AI architecture patterns, including AKS with ArgoCD, Databricks Apps, Foundry-based AI services, Nexus AI / Open Web UI, and other formally approved enterprise AI patterns.
• Ensure any new or additional AI platform pattern goes through architecture, SRM, security, operational, and cost review before adoption.
• Maintain practical design guidance that explains when to use each AI pattern, which guardrails apply, and what evidence is required for approval.
• Establish logging/telemetry standards and control verification
• Define guardrails for prompts, APIs, model usage, data access, output validation, logging, exception handling, and policy-as-code where practical.
• Ensure AI applications maintain audit-ready evidence, including architecture diagrams, data classification, risk tiering, control mapping, approvals, exception register, and review history.
• Drive Agentic AI and Zero Trust maturity through per-agent identity, workload identity, least privilege, JIT access, trust boundaries, memory protection, context isolation, and behavioral monitoring.

What your background should look like:

• AI services and agents: Strong grasp on AI / GenAI / Agentic AI foundational concepts, standard use cases and capabilities, security best practices for authentication / authorization / integration with tooling / data protection / skills / model security / threat protection / guardrails
• Security protocols and systems: Deep knowledge of cryptography, authentication, authorization, and network security principles
• Security tools: General awareness of capabilities and integration options for standard tools such as SIEM, IDS/IPS, DLP, endpoint protection, and vulnerability management systems
• Solid background on cloud and enterprise architecture: Experience with cloud security (AWS and Azure a must / Ali and Google nice to have), network and system architecture, and enterprise architecture frameworks (like TOGAF).
• Programming and scripting: Proficiency in scripting and programming languages is often required for automation and integration (e.g., Python, PowerShell, JavaScript, SQL).
• Operating systems: Strong knowledge of various operating systems like Windows, Linux, and UNIX.
• Compliance and regulations: Familiarity with industry standards and regulations such as ISO 42001, EU AI Act, NIST, GDPR.