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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Security Engineer - AI - R01573022 - **Company:** Brillio - **Location:** Seattle, WA, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Software Applications, Cloud Computing Security, Cyber Security, Data Governance, Information Leak Prevention, Data Mining, Fuzz Testing, R (Programming Language), Identity and Access Management, Intrusion Detection and Prevention, Information Systems Security Architecture Professional, Python (Programming Language), Machine Learning, Nmap, OAuth, Open Web Application Security, Platform as a Service (PAAS), Azure Machine Learning, SAP GRC, Security Software, Systems Integration, Wireshark, Web Applications, AI Infrastructure, Privacy Controls, Data Classification, Large Language Models, Multi-Agent Systems, Prompt Engineering, Software Security, Generative AI, Agentic-AI, Containerization, AI Platforms, Kubernetes, Metasploit, Deployment Automation, Transformer Architectures, Nessus, Machine Learning Operations, Appscan, Model Context Protocol, Burpsuite, Automation Anywhere, Devsecops, Security Orchestration, Automation & Response, Servicenow - **Published:** November 5, 2026 - **Apply:** https://www.careerbuilder.com/job-details/staff-security-engineer-ai-r01573022-seattle-wa--dce712ad-4194-45ca-b748-ff540a4411ba ## About the Role * We are looking for an experienced AI Security Engineer who can drive AI and LLM security initiatives, establish security standards, and implement robust technical controls across AI/ML platforms, agentic AI systems, and enterprise AI applications. This role combines strategic security leadership, hands-on engineering, AI infrastructure protection, and automation. The individual will work closely with AI platform, engineering, and security teams to define security roadmaps, mitigate emerging AI threats, and establish secure-by-design practices across the AI development lifecycle., + 7+ years of experience in information security, cybersecurity, or security engineering, including at least 2+ years focused on AI/ML security or Large Language Model (LLM) application security. + Strong understanding of AI/ML technologies, including: o Transformer architectures o Large Language Models (LLMs) o Retrieval-Augmented Generation (RAG) o Prompt Engineering o Agentic AI and autonomous AI workflows o 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-specific security threats, including: o Prompt Injection o Jailbreak Attacks o Model Poisoning o Model Manipulation o Sensitive Data Exposure and Data Extraction + Experience implementing threat detection, security architecture controls, and automated security mechanisms for AI/ML environments. + Proficiency in Python or Go, with experience developing production-grade integrations, security automation tools, and security pipelines. + Experience assessing third-party AI platforms, foundation models, and AI technology supply chains. + Working knowledge of AI governance frameworks, emerging regulatory requirements, data privacy, and Responsible AI principles. + Strong analytical and problem-solving skills, with the ability to proactively identify and address security gaps across complex AI/ML workflows. + Excellent communication and stakeholder management skills, with the ability to translate emerging AI security risks into actionable technical roadmaps and business impact for executive leadership., + Hands-on experience with Model Context Protocol (MCP) frameworks and autonomous multi-agent AI systems. + Strong understanding of AI-specific security and regulatory requirements, including: o Data residency and data localization requirements. o Data loss prevention and content protection controls for AI-generated outputs. o Permission boundaries and access restrictions for autonomous AI agents. o External OAuth authorization and access-scoping mechanisms. + Experience developing and implementing enterprise AI governance frameworks and AI enablement programs. + Background in designing and implementing AI platform security architectures. + Experience establishing security standards and governance models for enterprise-wide AI adoption. ## Description * SAP GRC, Nessus, Information Security & Cybersecurity, Cloud Security, Archer, Metasploit, Wireshark, Burpsuite, AWS Secrets Manager, Nmap, PaaS - Security, CxSAST, Web Application Scanning (WAS), ServiceNow GRC, AppScan, This role will focus on AI security strategy, engineering, governance, and automation. + AI Security Strategy and Roadmap: Define and drive the long-term AI and Agentic AI security roadmap, including security architecture, technical standards, and implementation strategies in collaboration with AI platform and engineering teams. + AI Infrastructure Security: Design and implement technical security controls to protect AI environments, including autonomous agent permissions, blast radius management, and advanced prompt injection detection. + Model Context Protocol (MCP) Security: Establish centralized security standards for Model Context Protocol (MCP) implementations and oversee secure, resilient MCP hosting infrastructure supporting autonomous AI agents. + AI Red Teaming and Adversarial Testing: Conduct adversarial security assessments and AI red teaming exercises across machine learning systems to identify vulnerabilities related to model manipulation, data extraction, and unauthorized access. + Threat Modeling and Architecture Reviews: Lead threat modeling initiatives and security architecture assessments for emerging AI capabilities, Retrieval-Augmented Generation (RAG) pipelines, autonomous workflows, and AI-powered applications. + Third-Party AI Security Assessments: Evaluate the security posture, privacy controls, data handling practices, and retention policies of third-party foundation models, AI platforms, and vector databases. + AI Risk Assessment and Compliance: Serve as a technical advisor for AI tooling evaluations and data classification initiatives. Develop standardized security assessment frameworks to ensure consistent risk management and compliance. + AI Security Governance: Establish and scale AI security governance frameworks, technical policies, and organizational standards to promote secure and responsible AI engineering practices. + Security Enablement: Develop and communicate practical AI security guidelines, helping engineering teams, business stakeholders, and leadership safely adopt and leverage AI technologies. + LLM Security and Runtime Protection: Architect, deploy, and automate real-time security controls for LLM inputs and outputs, including prompt injection prevention, jailbreak detection, and AI response validation.