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Senior Security Engineer, AI Incident Response

Snowflake - Menlo Park, CA, United States - In-office - posted 2026-08-11

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Snowflake is seeking a Senior Security Engineer to lead product-integrated incident response strategy with a primary focus on AI and LLM security. You will design and drive the implementation of incident response capabilities across Snowflake's AI product surface, including Cortex AI, Cortex Agents, Snowflake Intelligence, and supporting data pipelines. Key responsibilities include leading incident response for product-level security events with deep focus on AI-specific threat vectors such as prompt injection, model abuse, agent hijacking, and data exfiltration through AI workloads. You'll integrate IR into AI product pipelines by working directly with teams shipping Cortex features and AI-powered developer experiences to embed security requirements from design through deployment. You will develop and codify Snowflake's AI abuse response strategy, defining detection, containment, and remediation playbooks for LLM misuse, adversarial inputs, and AI-assisted attacks targeting customers. Additional responsibilities include addressing tech debt across the AI product stack, representing the IR team to cloud engineering and AI platform teams, securing modern AI-native codebases across multi-cloud environments (container-based inference services, RAG pipelines, vector stores, agent orchestration), and designing response capabilities built into Snowflake's AI operational infrastructure. You'll lead with data, code, and automation to build tooling that accelerates detection and response at scale. Required qualifications: 5+ years in information security (incident response, security engineering, or product/application security preferred), direct experience as incident commander for product-focused security incidents, experience leading or building application/security engineering programs with expertise in securing AI/ML systems, threat modeling and security testing across AI attack surfaces, familiarity with data governance challenges of LLMs and RAG architectures, working knowledge of cloud-native environments (AWS, Azure, GCP), SQL proficiency, Python experience, strong communication skills, and Bachelor's degree in Computer Science or equivalent. Bonus qualifications include experience securing AI/ML infrastructure, building agentic incident response capabilities, understanding of attacker TTPs including AI-specific techniques, CI/CD and secure release lifecycle patterns, and relevant certifications (GCIA, GCIH, GCSA, GDAT, CISSP/GISP, or cloud certifications).

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