AI Deployment Engineer, Cyber

Responsibilities

• Deeply embed with strategic customers as the technical lead for AI-enabled cybersecurity workflows, serving as a trusted partner to both security executives and technical practitioners.
• 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 so each audience understands value, risk, and practical next steps.
• Create reusable field assets such as demo narratives, playbooks, FAQs, objection handling, qualification guides, assessment templates, and competitive positioning.
• Validate, synthesize, and deliver 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 across one or more 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 how to design AI workflows with retrieval, structured outputs, tool use, evals, guardrails, telemetry, sandboxing, and human-in-the-loop review.
• Comfort 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, and avoid overstating model or security claims.
• Ownership of problems end-to-end, high throughput across multiple concurrent customer projects, and ability to know when to stay hands-on versus create reusable leverage for the broader field.