Senior Forward Deployed Engineer, Cyber
New
E
ElizaAI cybersecurity
Location: United States; Workplace: RemoteFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- CybersecurityCI/CDRESTful APIs
Requirements
- Bring deep practical cybersecurity expertise, including hands-on blue-team experience in detection, investigation, response, or defensive security engineering.
- Provide specific examples of cybersecurity decisions and outcomes personally owned.
- Have strong software engineering skills beyond security scripting, including production code, integrations, debugging, testing, and maintainable system design.
- Have taken complex technical work from problem definition through deployment, validation, and operational handover.
- Demonstrate practical AI fluency, including effective use of AI development tools and sound judgment about model evaluation, failure modes, and human review.
- Have experience deploying and troubleshooting software in cloud or customer-hosted environments.
- Understand identity and access, APIs, delivery pipelines, and production observability.
- Communicate clearly with customers and translate ambiguous needs into technical requirements and acceptance evidence.
- Work independently, make tradeoffs explicit, recognize the limits of your expertise, and escalate material risks early.
- Own useful outcomes, including verification, documentation, and adoption after the initial build.
- Preferred: additional depth in application security, AI agent security, security developer tooling, or purple-team and red-team work.
- Preferred: consulting, professional services, forward-deployed engineering, or comparable direct customer delivery experience.
Responsibilities
- Lead technical discovery with customer security and engineering teams to understand workflows, systems, data, risks, and operating constraints.
- Define the problem, baseline, architecture, and acceptance criteria, and make scope, dependencies, and tradeoffs visible.
- Write, review, and debug production software connecting models, APIs, telemetry, and security systems.
- Choose suitable tools and architecture, balancing speed, reliability, maintainability, and cost.
- Own deployment and troubleshooting in customer environments, including configuration, access, CI/CD, monitoring, and integration failures.
- Threat-model AI-enabled workflows and test realistic failure cases, including prompt injection, sensitive-data exposure, and unsafe tool actions.
- Build security controls across prompts, data flows, APIs, and cloud services, including least-privilege access, secrets protection, auditability, and human approval.
- Explain architecture, evidence, risks, and tradeoffs to technical and nontechnical stakeholders.
- Produce documentation, interfaces, runbooks, and knowledge transfer for customer teams.
- Turn proven solutions and lessons into reusable engineering patterns, evaluation cases, and playbooks.
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