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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 will 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'll 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 (including container-based inference services, RAG pipelines, vector stores, and agent orchestration layers), and partnering with AI and security engineering teams to provide expert guidance on secure architecture. You will design response capabilities built into Snowflake's AI operational infrastructure and lead with data, code, and automation to build tooling that accelerates detection and response at scale.
Required qualifications include 5+ years in information security (incident response, security engineering, or product/application security preferred), direct experience as incident commander for product-focused security incidents, and experience leading or building an application or security engineering program with a clear point of view on securing AI/ML systems. You should have experience with threat modeling and security testing across AI attack surfaces, familiarity with data governance and security challenges of LLMs and RAG architectures, working knowledge of cloud-native environments (AWS, Azure, GCP), SQL proficiency, and experience building automation with Python or similar languages. A Bachelor's degree in Computer Science or equivalent experience is required.
Bonus qualifications include experience securing AI/ML infrastructure, building agentic incident response capabilities, understanding current attacker TTPs including AI-specific techniques, familiarity with CI/CD and secure release lifecycle patterns, and relevant certifications (GCIA, GCIH, GCSA, GDAT, CISSP/GISP, or cloud certifications).
