Staff Security Engineer – AI - R01573022

Brillio•Seattle, WA

11d•$120,000 - $130,000

About The Position

Brillio is seeking an experienced AI Security Engineer to lead AI and LLM security initiatives, establish security standards, and implement technical controls across AI/ML platforms, agentic AI systems, and enterprise AI applications. The role combines strategic security leadership, hands-on engineering, AI infrastructure protection, and automation. The engineer will work with AI platform, engineering, and security teams to develop security roadmaps, address emerging AI threats, and advance secure-by-design practices throughout the AI development lifecycle. The position focuses on AI security strategy, engineering, governance, and automation. Responsibilities include securing AI infrastructure and autonomous agents, defining standards for Model Context Protocol implementations, conducting AI red teaming and threat modeling, assessing third-party AI services, and deploying runtime protections for LLM inputs and outputs. The role also provides guidance on AI risk, compliance, governance, and secure adoption across engineering teams and business stakeholders.

Requirements

At least 7 years of experience in information security, cybersecurity, or security engineering, including at least 2 years focused on AI/ML security or LLM application security.
Strong understanding of transformer architectures, LLMs, RAG, prompt engineering, agentic AI, autonomous AI workflows, and the OWASP Top 10 for LLM Applications.
Deep technical expertise in cloud security, particularly AWS and GCP, including IAM, containerization, Kubernetes, and DevSecOps methodologies.
Hands-on experience with AI/ML security architectures, production AI deployments, and securing the end-to-end AI development lifecycle.
Strong knowledge of AI security threats, including prompt injection, jailbreak attacks, model poisoning, model manipulation, sensitive-data exposure, and data extraction.
Experience assessing third-party AI platforms, foundation models, and AI technology supply chains.
Proficiency in Python or Go, including experience developing production-grade integrations, security automation tools, and security pipelines.
Working knowledge of AI governance frameworks, emerging regulatory requirements, data privacy, and Responsible AI principles.
Strong analytical and problem-solving skills, including the ability to identify and address security gaps across complex AI/ML workflows.
Excellent communication and stakeholder-management skills, including the ability to communicate AI security risks and technical roadmaps to executive leadership.

Nice To Haves

Hands-on experience with Model Context Protocol (MCP) frameworks and autonomous multi-agent AI systems.
Understanding of AI-specific security and regulatory requirements, including data residency and localization, data loss prevention and content protection for AI-generated outputs, permission boundaries for autonomous agents, and external OAuth authorization and access scoping.
Experience developing and implementing enterprise AI governance frameworks and AI enablement programs.
Background designing and implementing AI platform security architectures.
Experience establishing security standards and governance models for enterprise-wide AI adoption.

Responsibilities

Define and drive the long-term AI and Agentic AI security roadmap, including security architecture, technical standards, and implementation strategies with AI platform and engineering teams.
Design and implement controls for AI environments, including autonomous-agent permissions, blast-radius management, and prompt-injection detection.
Establish centralized security standards for Model Context Protocol (MCP) implementations and oversee secure, resilient MCP hosting infrastructure for autonomous AI agents.
Conduct adversarial assessments and AI red-team exercises to identify vulnerabilities involving model manipulation, data extraction, and unauthorized access.
Lead threat modeling and security architecture assessments for AI capabilities, RAG pipelines, autonomous workflows, and AI-powered applications.
Assess third-party foundation models, AI platforms, and vector databases, including their security posture, privacy controls, data handling, and retention policies.
Advise on AI tooling evaluations and data classification, and develop standardized security assessment frameworks for risk management and compliance.
Establish and scale AI security governance frameworks, technical policies, and organizational standards for secure and responsible AI engineering.
Develop and communicate practical AI security guidelines for engineering teams, business stakeholders, and leadership.
Architect, deploy, and automate real-time LLM security controls, including prompt-injection prevention, jailbreak detection, and AI response validation.
Implement threat detection, security architecture controls, and automated security mechanisms for AI/ML environments.
Translate emerging AI security risks into actionable technical roadmaps and business impact for executive leadership.