Senior Security Engineer, Incident Response

Responsibilities

• Lead incident response for product-level security events, focusing on AI-specific threats such as prompt injection, model abuse, agent hijacking, and data exfiltration through AI workloads.
• Integrate incident response into AI product pipelines across Cortex AI, Cortex Agents, Snowflake Intelligence, and AI-powered developer experiences.
• Define detection, containment, and remediation playbooks for LLM misuse, adversarial inputs, and AI-assisted attacks.
• Ensure Cortex and agentic architectures meet incident-response readiness requirements.
• Represent incident response across cloud engineering, AI platform, corporate security, and customer-facing teams.
• Secure AI-native codebases across multi-cloud environments, including containerized inference services, RAG pipelines, vector stores, and agent orchestration layers.
• Guide secure architecture for AI features and customer-facing AI capabilities.
• Build and manage response capabilities across model-serving endpoints, Cortex Search indexes, and Snowpark ML pipelines.
• Develop automation and tooling to accelerate detection and response for product security incidents.

Requirements

• 5+ years of experience in information security, incident response, security engineering, or product/application security.
• Experience serving as incident commander for product-focused security incidents.
• Experience building or leading an application or security engineering program, with a perspective on securing AI/ML systems.
• Experience with threat modeling and security testing across AI attack surfaces, including prompt injection, indirect injection, model inversion, embedding extraction, and AI dependency supply-chain attacks.
• Familiarity with data governance and security challenges involving LLMs, RAG architectures, and agentic systems.
• Working knowledge of AWS, Azure, and GCP, plus SaaS and AI platform threats.
• SQL proficiency and experience building automation with programming languages, preferably Python.
• Strong communication skills and ability to translate security risks into actionable product guidance.
• Bachelor's degree in Computer Science or a related field, or equivalent experience.

Nice-to-haves

• Experience securing AI/ML infrastructure, including model serving, vector databases, embedding pipelines, API gateways, and LLM-integrated applications.
• Experience building agentic incident-response capabilities, including skills, agents, and pipelines.
• Understanding of attacker TTPs, adversarial ML, agent manipulation, and LLM jailbreaking.
• Familiarity with CI/CD and secure release lifecycle patterns for AI feature pipelines.
• GCIA, GCIH, GCSA, GDAT, CISSP, GISP, AWS, Azure, or GCP certifications.