Senior Security Engineer, AI Incident Response
Leads product security incident response for Snowflake’s AI products and infrastructure, developing detection, containment, and remediation capabilities for LLM and agentic threats. Requires 5+ years in security, incident command experience, cloud-native expertise, and strong knowledge of AI attack surfaces.
About the job
Responsibilities
- Lead incident response for product-level security events, focusing on AI-specific threats including prompt injection, model abuse, agent hijacking, and data exfiltration through AI workloads.
- Integrate incident response into AI product pipelines across Cortex features, Snowflake Intelligence, and AI-powered developer experiences.
- Develop detection, containment, and remediation playbooks for LLM misuse, adversarial inputs, and AI-assisted attacks.
- Improve incident-response readiness across Cortex and agentic architectures.
- Represent incident response with cloud engineering, AI platform, corporate security, and customer-facing teams.
- Secure AI-native codebases across multi-cloud environments, including container-based inference services, RAG pipelines, vector stores, and agent orchestration layers.
- Advise AI and security engineering teams on secure architecture for high-impact AI features.
- Design and manage response capabilities across model-serving endpoints, Cortex Search indexes, and Snowpark ML pipelines.
- Build 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 expertise in 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 associated with LLMs, RAG architectures, and agentic systems.
- Working knowledge of AWS, Azure, or Google Cloud and the threat landscape for SaaS and AI platforms.
- SQL proficiency and experience building automation with common programming languages, preferably Python.
- Strong communication skills and the 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 in enterprise contexts.
- Familiarity with CI/CD and secure release lifecycle patterns for AI feature pipelines.
- Certifications such as GCIA, GCIH, GCSA, GDAT, CISSP/GISP, or AWS, Azure, or Google Cloud certifications.
Compensation
- Annual salary range: $176,000–$253,000.
Skills
Incident Response, Ai Security, Llm Security, Threat Modeling, Security Testing, Python, SQL, AWS, Azure, GCP, RAG, Vector Databases, CI/CD, Prompt Injection, Kubernetes
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