About this opportunity
Snowflake lists this Senior Security Engineer, Incident Response opportunity in bellevue, Kentucky. Review the employer’s description below for duties, qualifications and application requirements.
Job description
We are hiring a Senior Security Engineer, dedicated to Product Security Incident Response
In this role, you will lead and architect Snowflake’s product-integrated Incident Response strategy, with a primary focus on AI and LLM security
You’ll design, plan, and drive the implementation of incident response capabilities across Snowflake’s AI product surface - including Cortex AI, Cortex Agents, Snowflake Intelligence, and the data pipelines that power them
Lead incident response for product-level security events, with deep focus on AI-specific threat vectors including prompt injection, model abuse, agent hijacking, and data exfiltration through AI workloads
Integrate IR into AI product pipelines - work directly with teams shipping Cortex features, Snowflake Intelligence, and AI-powered developer experiences to embed security requirements from design through deployment
Develop and codify our AI abuse response strategy - defining detection, containment, and remediation playbooks for LLM misuse, adversarial inputs, and AI-assisted attacks targeting Snowflake customers
Address tech debt across the AI product stack, ensuring that new Cortex and agentic architectures meet IR readiness requirements from the ground up
Represent the IR team to cloud engineering, AI platform teams, corporate security, and customer-facing business units
Secure modern AI-native codebases operating across multi-cloud environments - including container-based inference services, RAG pipelines, vector stores, and agent orchestration layers
Partner with world-class AI and security engineering teams, providing expert guidance on secure architecture for high-impact AI features and customer-facing AI capabilities
Design and manage response capabilities built into Snowflake’s AI operational infrastructure - from model serving endpoints to Cortex Search indexes and Snowpark ML pipelines
Lead with data, code, and automation - build tooling that accelerates detection and response for product security incidents at Snowflake scale
Drive meaningful security outcomes for the customers and enterprises trusting Snowflake with their most sensitive data and AI workloads
Benefits
Comprehensive health insurance plans
Health savings accounts
Robust retirement plans
Life and disability insurance
Weekly online lunch and learns
Virtual workout classes
Ergonomic work-from-home equipment
On-demand mental health and wellness programs
Fertility benefits and family planning resources
Generous time-off and various leave plans
Onsite and Remote Work
Employee discounts and pre-tax selections
New hire equity + Employee Stock Purchase Plan (ESPP)
Quarterly bonus or commission program
Strong communication skills, with the ability to translate security risk into actionable guidance for product teams
Empathy for developer experience, helping AI engineers ship securely rather than slowing them down
SQL proficiency, plus experience building automation and tools with common programming languages (Python preferred)
Experience leading or actively building an application or security engineering program, with a clear point of view on securing AI/ML systems
Direct experience serving as incident commander for product focused security incidents
Working knowledge of cloud-native environments (AWS, Azure, GCP) and the threat landscape specific to SaaS and AI platforms
5+ years of experience in information security, primarily in incident response, security engineering, or product/application security (preferred)
Experience with threat modeling and security testing across AI attack surfaces, including prompt injection, indirect injection, model inversion, embedding extraction, and supply chain attacks on AI dependencies
Bachelor’s degree in Computer Science or a related field, or equivalent experience
Familiarity with the unique data governance and security challenges introduced by LLMs, RAG architectures, and agentic systems
Experience securing AI/ML infrastructure, including model serving, vector databases, embedding pipelines, API gateways, and LLM-integrated application architectures
Experience building agentic incident response capabilities, including skills, agents, and pipelines
Familiarity with CI/CD and secure release lifecycle patterns, with an emphasis on building security into AI feature pipelines
Understanding of current attacker TTPs, including emerging AI-specific techniques such as adversarial ML, agent manipulation, and LLM jailbreaking in enterprise contexts
Preferred certifications: GCIA, GCIH, GCSA, GDAT, CISSP/GISP, or cloud certifications (AWS, Azure, GCP)
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Who can apply
Review the original listing for work authorization, qualifications and employer requirements.