СШАStaff Security Engineer – AI - R01573022
1 нед. назад
120k–130k USD / годСШАLeadOnsite
Staff Security Engineer role focusing on AI technologies, responsible for securing AI systems and infrastructure.
О компании
- About : is one of the fastest growing digital technology service providers and a partner of choice for many Fortune 1000 companies seeking to turn disruption into a competitive advantage through innovative digital adoption. , renowned for its world-class professionals, referred to as "Brillians", distinguishes itself through their capacity to seamlessly integrate cutting-edge digital and design thinking skills with an unwavering dedication to client satisfaction. takes pride in its status as an employer of choice, consistently attracting the most exceptional and talented individuals due to its unwavering emphasis on contemporary, groundbreaking technologies, and exclusive digital projects. 's relentless commitment to providing an exceptional experience to its Brillians and nurturing their full potential consistently garners them the Great Place to Work® certification year after year. Staff Security Engineer – AI
Требования
- 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. Key Responsibilities 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.
- 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: Transformer architectures
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Prompt Engineering
- Agentic AI and autonomous AI workflows
- 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: Prompt Injection
- Jailbreak Attacks
- Model Poisoning
- Model Manipulation
- 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: Data residency and data localization requirements.
- Data loss prevention and content protection controls for AI-generated outputs.
- Permission boundaries and access restrictions for autonomous AI agents.
- 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.
Другое
- 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