AI Deployment Engineer, Cyber

What you'll do

• Serve as technical lead for AI-enabled cybersecurity workflows embedded with strategic customers
• Lead discovery across AppSec, DevSecOps, vulnerability management, SOC/IR, detection engineering, red team, cloud security, identity, and GRC automation use cases
• Build and deliver customer-facing demos, prototypes, workshops, proofs of concept, and reference architectures using OpenAI APIs, Codex, agents, scripts, CLIs, GitHub workflows, CI/CD systems, logs, tickets, scanners, and common security tools
• Scope pilots with clear success criteria, data requirements, workflow integrations, evaluation methods, security constraints, safety boundaries, and human approval points
• Advise customers on safe implementation patterns including tool and function calling, structured outputs, retrieval, sandboxing, data handling, guardrails, telemetry, auditability, and approval-gated side effects
• Translate between CISO-level outcomes and practitioner-level implementation details
• Create reusable field assets such as demo narratives, playbooks, FAQs, objection handling, qualification guides, assessment templates, and competitive positioning
• Provide high-signal feedback to Product, Engineering, Research, Security, and GTM teams based on recurring customer requirements, blockers, product gaps, and emerging cyber workflows

Requirements

• 5+ years of technical consulting, solutions engineering, security architecture, cyber advisory, deployment engineering, professional services, or equivalent customer-facing technical experience
• Strong cybersecurity domain expertise in areas such as application security, cloud security, identity, vulnerability management, secure SDLC, incident response, detection engineering, threat intelligence, red teaming, or security architecture
• Ability to communicate credibly with CISOs, CTOs, security executives, engineering leaders, and highly technical security practitioners
• Hands-on experience building prototypes or production systems with APIs, Python or JavaScript, agents, scripts, CLIs, GitHub workflows, CI/CD systems, logs, tickets, scanners, or other common security tooling
• Understanding of AI workflow design including retrieval, structured outputs, tool use, evals, guardrails, telemetry, sandboxing, and human-in-the-loop review
• Comfortable scoping pilots from ambiguous customer pain including success metrics, required data, workflow integrations, evaluation criteria, deployment assumptions, and decision gates
• Evidence-first security judgment: validate findings, separate true positives from noise, document assumptions, avoid overstating model or security claims
• Ability to own problems end-to-end, operate with high throughput across multiple concurrent customer projects, and balance hands-on work with creating reusable leverage
• Humble attitude, eagerness to help colleagues, and desire to ensure team and customer success

Tech stack

Benefits