AI Security Architect

We are currently seeking a AI Security Architect – Dallas onsite to join our team in Dallas, Texas (US-TX), United States (US).

Experience level: 10+ years

We are seeking an experienced and highly skilled AI Security architect who will define security architecture and implement robust security controls for our AI/ML systems and their underlying platforms. The architect will serve as the team’s technical mentor and architecture authority, driving secure‑by‑design patterns across the AI/ML lifecycle (data, training, evaluation, deployment, and production monitoring) and proactively mitigating AI‑specific threats such as model integrity risks, data poisoning, adversarial attacks, prompt injection, model extraction, and inference‑time abuse.

The role leads technically, sets standards, and guides engineers day‑to‑day through architecture, reviews, and delivery. Ensures AI systems are secure, compliant, and resilient by implementing data protection, threat detection, guardrails, and ongoing risk monitoring across the AI lifecycle.

• AI Platform Admin (M365, copilot Studio) – manages AI platforms and environments, including access provisioning, governance controls, and policy enforcement (e.g., DLP, security, and compliance).
• AI Reusable Utility – develops reusable components (e.g., prompts, connectors, APIs, templates) to accelerate AI solution delivery and promote standardization across use cases.
• AI Common Infrastructure, Framework & Observability Architect (AWS and Azure) – designs and maintains the foundational AI infrastructure, frameworks, and observability capabilities (telemetry, monitoring, metrics) required for scalable, reliable, and governed AI operations.

• Agent Security
• Non‑Human Identity & Access – define strict role‑based access control (RBAC) and least‑privilege models for AI agents using identity systems (e.g., Entra Agent ).
• Guardrails & Sandboxing – design runtime environments with restricted permissions to prevent manipulated agents from accessing unauthorized APIs, data sources, or executing malicious tool chains.
• Input/Output Protection – implement defenses against adversarial attacks, prompt injections, jail breaking, and sensitive data leakage (DLP) across agent workflows.
• Observability & Monitoring
• Decision traceability – architect logging and monitoring standards to map how reasoning agents use data and call APIs, eliminating “black box” decisions.
• Model drift & integrity – monitor models and prompt templates in production to detect behavioral drift, anomalies, and poisoning or evasion attacks.
• SOC Monitoring & Automation
• Autonomous Security (AI SOC) – design LLM‑driven and agentic workflows to improve alert triage, contextual correlation, false‑positive filtering, and playbook automation.
• Incident Response Playbooks – establish remediation strategies and threat‑hunting procedures for AI‑specific events (e.g., compromised model artifacts, hallucination‑driven exploits).

• Regulatory Alignment – map AI‑specific controls to established standards like the NIST AI RMF, OWASP Top 10 for LLMs, and GDPR.
• Audit Readiness – build audit pipelines that track and explain everything an agent does to satisfy ongoing AI regulatory compliance and governance requirements.
• Define and maintain AI security reference architectures for multiple AI deployment patterns, including MCP / Agentic AI and LLM application stacks (RAG, tools/plugins, agents, orchestration).
• Establish and evolve security requirements, patterns, and guardrails across the AI/ML SDLC (design → build → run), including secure pipelines and platform controls.
• Own AI security architecture decisions across critical domains: identity, secrets, data protection, network controls, tenancy boundaries, logging/telemetry, and isolation for training/inference.

• Design and deploy controls to ensure model integrity and governance, including RBAC/ABAC for models, feature stores, data sets, registries, and evaluation artifacts.
• Build/enable technical…