Principal AI Security Engineer
New
C
Cerebras SystemsAI Infrastructure
Remote, California, United States; Sunnyvale CA or Toronto CanadaFull-TimePrincipal
Salary not disclosed
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Job Details
- Experience
- 10+ years
- Required Skills
- AWSPythonKubernetesCI/CD
Requirements
- 10+ years of experience in security engineering, platform security, infrastructure security, product security, or related technical security roles.
- Strong hands-on engineering ability in Python and at least one additional production language.
- Experience designing, building, operating, and improving security controls as code.
- Strong cloud and infrastructure security experience, preferably with AWS, including IAM, networking, secrets management, logging, and cloud-native control planes.
- Deep understanding of identity and access systems, including SSO, MFA, OAuth, service accounts, workload identity, authorization, privileged access, and least privilege.
- Practical experience securing runtime environments such as containers, Kubernetes, isolated workloads, secure development environments, distributed compute platforms, or production service infrastructure.
- Familiarity with AI security, LLM application security, agentic workflows, MCPs, prompt injection, autonomous coding agents, or AI platform security.
- Ability to reason about cross-system risk involving identity, data, models, tools, networks, workflows, approvals, and automation.
- Strong written communication skills and the ability to influence senior technical stakeholders across Security, Product, IT, Infrastructure, and Engineering.
Responsibilities
- Define security architecture and build controls for AI platforms, training and inference workflows, model-serving systems, customer workloads, developer workflows, and agentic systems.
- Develop reusable AI and agent security patterns for identity, authorization, delegated authority, scoped tool access, MCPs, connectors, secrets, approvals, isolation, and auditability.
- Design runtime controls that constrain execution, access, data exposure, model and tool interaction, and blast radius.
- Build security capabilities as code using infrastructure as code, configuration as code, policy as code, GitOps, CI/CD, and automated validation.
- Define secure development patterns for AI systems, agents, prompts, tools, models, policies, evaluations, releases, and rollback.
- Automate security reviews, policy checks, evidence collection, control validation, and remediation.
- Instrument AI, agent, and platform activity with telemetry, traceability, policy decisions, audit logs, anomaly signals, and response workflows.
- Lead hands-on security reviews and influence product, platform, infrastructure, and security architecture through practical design changes and reusable controls.
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