Senior Staff AI Security Lead
New
I
IonQAI security
Location: Remote, US; We are open to a fully remote option for the right candidate.Full-TimeLead
Salary$180,000 — $225,000 USD
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Job Details
- Experience
- 8+ years in security engineering, platform engineering, or a closely related technical field, including significant hands-on software development.
- Required Skills
- PythonLLMDistributed Systems
Requirements
- 8+ years in security engineering, platform engineering, or a closely related technical field, including significant hands-on software development.
- Demonstrated experience building and operating production systems with large language models.
- Strong software engineering fundamentals and fluency in Python or an equivalent language.
- Experience with API design, distributed systems, and cloud infrastructure such as AWS, GCP, or Azure.
- Deep understanding of identity and access management, secrets management, network boundaries, logging and detection, and secure software development practices.
- Working knowledge of AI-specific risks, including prompt injection, jailbreaks, data leakage through model outputs, insecure agent tool use, and model and dependency supply-chain risks, plus practical mitigations.
- A track record of driving technical change through influence, building consensus, teaching, and shipping solutions people adopt.
- Clear written and verbal communication, including explaining system designs to engineers and business cases to executives.
- Sound judgment about where AI is useful and where it is not.
- Preferred: experience with agent frameworks, orchestration, and MCP or comparable tool-use standards.
- Preferred: experience building evaluation frameworks or LLM observability tooling, retrieval systems, embeddings, vector databases, or knowledge pipelines.
- Preferred: security operations, detection engineering, or incident response background; familiarity with NIST AI Risk Management Framework, ISO/IEC 42001, or OWASP Top 10 for LLM Applications.
Responsibilities
- Design and build a shared platform for security teams to develop AI applications, including 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-in-the-loop approval gates, and least-privilege agent credentials.
- Connect security knowledge to AI systems through governed retrieval pipelines with access controls and data classification enforcement.
- Build evaluation harnesses, regression suites, and observability for output quality, latency, cost, hallucination rate, and drift.
- Implement guardrails against prompt injection, data exfiltration through model outputs, insecure tool use, and AI supply-chain risk; red-team the platform and applications.
- Own the reference architecture, golden paths, and internal documentation for security AI development.
- Identify AI use cases across detection engineering, incident response, threat intelligence, vulnerability management, GRC, and security operations; deliver flagship applications.
- Run enablement programs, define adoption and impact metrics, and partner with Legal, Privacy, and Compliance on AI governance.
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