Vulnerability Management Engineer (AI Automation)

Role overview

Build and operate vulnerability management processes across cloud, hybrid, container, and application environments. This hands-on security engineering role combines risk-based prioritization with AI-powered automation to improve asset discovery, validate findings, coordinate remediation, and report risk to leadership.

Responsibilities

- Oversee ongoing asset discovery, vulnerability assessment, remediation verification, and risk reporting. - Prioritize findings using severity scores, exploit likelihood, active threat intelligence, and business importance. - Develop AI agents and multi-step language-model workflows for asset reconciliation, false-positive reduction, exploitability analysis, ticket routing, and escalation. - Coordinate remediation with IT, DevOps, and software teams; establish response procedures for critical zero-day vulnerabilities and manage exceptions or risk acceptance. - Define remediation governance and service-level targets aligned with security and regulatory frameworks. - Track and present measures such as scan coverage, remediation time, aging findings, and SLA adherence.

Requirements

- At least five years in information security, particularly vulnerability, application, or cloud security. - Practical experience with vulnerability, cloud security, or application security tools and automating scans, telemetry ingestion, and remediation checks. - Experience building autonomous AI agents or multi-step LLM workflows, and using retrieval-augmented generation to correlate findings with code, assets, and threat data. - Strong Python skills, including production automation and REST or GraphQL integrations. - Experience connecting security tools with ticketing, SOAR, asset management, or collaboration systems. - Knowledge of CVSS, EPSS, CISA KEV, cloud and container environments, CI/CD, and relevant standards such as NIST SSDF, EU CRA, or IEC.