Sr./Staff AI Security Engineer

Key Responsibilities

I. AI Security Risk Assessment

AI Threat Modeling & Risk Assessment: Lead the analysis and assessment of security and privacy risks across the evolving AI landscape, including large language models (LLMs), Generative AI services, agentic and multi-agent systems, and AI data governance

Policy and Compliance: Collaborate with legal and compliance teams to ensure AI applications adhere to internal policies and external regulations (e.g., NIST AI RMF, EU AI Act, emerging AI-specific laws)

Vulnerability Analysis: Study and document potential vulnerabilities within AI ecosystems and pipelines, including model integrity, training data exposure/leakage, inference endpoints, and agentic behavior risks

Remediation Design: Design and recommend effective security controls and mitigations to address identified risks, translating security requirements into actionable engineering plans

II. AI Security Implementation & Engineering

Security Service Implementation: Lead the technical implementation of specific AI-related security services, such as input/output content filters, adversarial defense mechanisms, and secure model serving architectures

AI System & Data Security Implementation: Lead the design and implementation of security controls across AI systems — encompassing data governance, model integrity, access controls, usage policy enforcement, and compliance auditing — and embed security throughout the full AI Development Lifecycle (AI DLC), from data ingestion and model integration through to deployment and monitoring, championing a "security-by-design" approach across all AI initiatives

Research & Advisory: Stay current with the rapidly evolving field of AI security and attack vectors. Provide expert consultation to product and engineering teams on best practices for secure AI development

Required Qualifications

Bachelor's or Master's degree in Computer Science, Cybersecurity, or related field

5+ years of experience in software engineering, with at least 2 years hands-on experience building and deploying AI applications or AI platform engineering in production

Proficiency in Python; experience with LLM APIs, prompt/context engineering, and AI agent or orchestration frameworks

Practical experience with AI tooling: coding assistants (GitHub Copilot, Claude Code, Cursor, etc.), agentic runtimes, and CI/CD pipeline integration

Familiarity with cloud platforms (AWS, Azure, or GCP) and containerization (Docker, Kubernetes)

Solid understanding of AI security risk concepts (e.g., prompt injection, data leakage, model misuse, agentic behavior risks)

Strong communication skills — able to translate AI risk findings into clear, actionable language for both technical and non-technical stakeholders

Preferred Qualifications

Experience in AI/ML security, MLSecOps, or cloud-native security

Familiarity with AI governance frameworks (NIST AI RMF, ISO 42001, EU AI Act) or security frameworks (NIST CSF, ISO 27001)

Experience with shadow AI detection, NHI/agent identity management, or AI observability tooling

Relevant certifications: CISSP, CISM, CRISC, or CAISP

Familiarity with software supply chain security as it applies to AI models and training data

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連結智慧 守護世界 --- Connected Intelligence for Securing a Connected World