Principal AI Security Technologist

Micron is seeking a Principal AI Security Technologist to lead the next phase of a security transformation that is already underway. You will own the target-state AI and data architecture for Micron's converged security operations, spanning Physical Security, SOC/GSOC, Threat Intelligence, and Insider Risk, and drive AI agents and automation from pilot into production at global scale. In this role you will be a part of a team that will be redesigning how security work gets done. The foundation is in place: a unified security data platform bringing together access control, video, alarm, and sensor data; early AI agents supporting operator triage and investigation; and a roadmap toward risk-based, intelligence-led operations. We need a Principal AI Security Technologist to take that foundation and scale it into a fully converged, AI-supported security ecosystem. This role is chartered to fundamentally change how security work is performed, not to maintain tools or make incremental improvements. You will decide which work machines should do, which work people should do, and how the two fit together, then deliver it! Responsibilities: • Own the security transformation roadmap. Carry forward and evolve the multi-year roadmap that moves Physical Security, SOC/GSOC Operations, Threat Intelligence, and Insider Risk from reactive, manual processes to technology-enhanced, intelligence-led capabilities. Keep it tied to operational priorities and measurable outcomes. • Set the converged security architecture. Define and govern the target-state AI and data architecture so that physical security systems, SOC tooling, intelligence platforms, and insider risk capabilities operate as one ecosystem. Make the hard calls on data models, integration patterns, platform choices, and where AI should and should not sit in the workflow. • Scale AI agents and automation. Identify security tasks suited to AI agent automation or AI-assisted execution, including triage, enrichment, correlation, and investigation preparation. Take agent capabilities from pilot to production with the evaluation, guardrails, and human oversight needed for security-critical decisions. • Shift from alerts to risk. Lead the move from rule-based alerting to risk-based assessment and prioritization across physical, intelligence, and insider domains. Use multi-signal correlation to detect emerging threats earlier and with fewer false positives. • Deliver and measure outcomes. Lead initiatives from concept through production and enterprise scale. Define and track success metrics for workload reduction, detection accuracy, response speed, and risk visibility. Retire legacy tools and manual workflows as new automated capabilities mature. • Provide technical leadership. Mentor engineers and data scientists on the team, partner with IT, cybersecurity, legal, and privacy team members, and represent the program's technical direction to senior leadership. Minimum Qualifications: • 10+ years in security technology, security engineering, or security operations, including significant time in an architect or technical lead role. • Hands-on experience applying AI agents, machine learning, or advanced analytics to security or adjacent operational problems, with at least one capability delivered to production. • Strong understanding of enterprise physical security systems (access control, video management, intrusion and alarm systems) and security operations center workflows. • Experience designing data and integration architectures that bring together data from multiple operational systems. • Demonstrated ability to translate security risk and operational needs into technical and automated solutions, and to lead multi-functional teams through delivery. Preferred Qualifications: • Experience with converged security, fusion-center operations, and high-security industrial environments, including semiconductor or manufacturing. • Experience deploying LLM-based agents in production, including evaluation, guardrails, and human-in-the-loop design. • Familiarity with cloud data platforms, data lakes, and lakehouse architectures. • Knowledge of security risk frameworks, governance, privacy, and responsible AI, particularly for workforce and video data. • Relevant security, cloud, or technology certifications such as CISSP, CPP, PSP, or cloud architecture credentials, advanced education, or equivalent experience.