Senior Staff AI Security Lead

Responsibilities

• Design and build the enterprise AI platform for model gateway and routing, authentication and authorization, secrets handling, rate limiting, cost attribution, and audit logging.
• Establish reusable patterns for agentic workflows, including tool and MCP server integration, sandboxed execution, human approval gates, and least-privilege agent credentials.
• Connect security knowledge sources to AI systems through governed retrieval pipelines with access controls and data classification enforcement.
• Build evaluation harnesses, regression suites, observability, and quality baselines covering output quality, latency, cost, hallucination, and drift.
• Implement guardrails against prompt injection, model-output data exfiltration, insecure tool use, and model and dependency supply-chain risk; red-team the platform and applications.
• Own the reference architecture, golden paths, internal documentation, and standards for security AI development.
• Identify high-value AI use cases across detection engineering, incident response, threat intelligence, vulnerability management, GRC, and security operations.
• Build production AI applications, define adoption and impact metrics, and lead workshops, office hours, documentation, and other enablement programs.
• Partner with Legal, Privacy, and Compliance on acceptable-use policies, review processes, and AI risk frameworks.
• Evaluate vendors and open models and maintain the organization's technical perspective on AI capabilities, security threats, and roadmap implications.

Requirements

• 8+ years in security engineering, platform engineering, or a closely related technical field, including significant hands-on software development.
• Production experience building and operating systems with large language models, including experience handling failures and iterating on deployed systems.
• Strong software engineering fundamentals and fluency in Python or an equivalent language, including API design, distributed systems, and cloud infrastructure.
• Deep understanding of identity and access management, secrets management, network boundaries, logging and detection, and secure software development practices.
• Working knowledge of prompt injection, jailbreaks, model-output data leakage, insecure agent tool use, model supply-chain risk, dependency supply-chain risk, and practical mitigations.
• Track record of driving technical change through influence, consensus-building, teaching, and delivering systems that users adopt.
• Clear written and verbal communication for both technical and executive audiences, along with sound judgment about appropriate AI use.
• Preferred qualifications include experience with agent frameworks, orchestration, MCP or comparable tool-use standards, LLM evaluation or observability tooling, security operations, detection engineering, incident response, retrieval systems, embeddings, vector databases, knowledge pipelines, AI governance, and open-weight model fine-tuning or evaluation.
• Familiarity with the NIST AI Risk Management Framework, ISO/IEC 42001, or the OWASP Top 10 for LLM Applications is preferred.
• Experience as a first or founding hire for a platform or capability that later scaled organization-wide is preferred.

Benefits

• Onsite, hybrid, or fully remote work for the right candidate, with travel up to 10%.
• Comprehensive medical, dental, and vision plans.
• Matching 401(k), unlimited PTO, and paid holidays.
• Parental and adoption leave, legal insurance, and a home technology stipend.
• The total compensation package also includes bonus and equity; participation details are provided with an offer.