Director, AI Engineering

Job Description

Role Summary

The Director, Cyber AI Engineering is a senior technical leader responsible for architecting and delivering production-grade AI systems that power autonomous cyber defense and enterprise security automation.

Reporting to the Director of Cyber AI & Automation, this role leads the design and implementation of scalable AI decision engines, behavioral analytics systems, and agent-based automation integrated directly into enterprise security platforms.

This is a hands-on technical leadership role. The Director will design systems, guide engineering execution, and ensure AI capabilities are production-ready, governed, and safely integrated into enforcement workflows.

Core Responsibilities

AI Systems Architecture

• Design scalable AI architectures capable of ingesting and reasoning over high-volume enterprise telemetry.

• Lead engineering of behavioral analytics, anomaly detection, and risk scoring models.

• Define technical patterns for AI integration into enforcement platforms.

Autonomous Decisioning & Enforcement Integration

• Architect AI decision engines that drive conditional actions (e.g., isolation logic, adaptive controls, escalation workflows).

• Ensure safe deployment through guardrails, kill switches, rollback mechanisms, and critical system protections.

• Collaborate with Platform Engineering to operationalize AI outputs in Defender XDR, Sentinel, and related systems.

Advanced Data & Model Engineering

• Guide large-scale data fusion across identity, endpoint, cloud, and threat intelligence telemetry.

• Establish model evaluation, validation, and performance monitoring frameworks.

• Optimize systems for performance, scalability, and resilience at enterprise scale.

Agentic AI & Automation Systems

• Lead development of multi-agent AI systems for operational optimization and workflow automation.

• Establish standards for prompt engineering, reasoning reliability, and agent evaluation.

• Translate complex operational challenges into structured AI solutions.

Technical Leadership

• Serve as senior technical authority within the Cyber AI Engineering vertical.

• Mentor AI and data engineers in production systems design.

• Contribute to roadmap definition and technical prioritization.

• Communicate system design and risk tradeoffs to senior stakeholders as needed.

Required Qualifications

• 10+ years of experience in AI engineering, data science systems, or large-scale analytics platforms.

• Proven experience architecting and deploying production AI systems.

• Strong expertise in Python and distributed data systems.

• Experience designing high-scale data pipelines and cloud-native architectures.

• Deep understanding of anomaly detection, behavioral modeling, or dynamic risk scoring.

• Experience leading technical teams and complex system integration efforts.

• Advanced degree in Computer Science, Engineering, or related discipline (MS required; PhD strongly preferred).

Preferred Qualifications

• Experience in cybersecurity analytics or cyber risk platforms.

• Experience with Microsoft Defender XDR, Sentinel, or similar enterprise security platforms.

• Experience integrating AI outputs into automated enforcement systems.

• Background in systems engineering, operations research, or large-scale network analytics.

• Experience operating in regulated or mission-critical environments.

What Success Looks Like

Within 6 months:

• AI architecture patterns established for scalable, production-ready systems.

• At least one AI decision engine integrated into a live enterprise workflow.

• Model evaluation and governance framework implemented.

Within 12 months:

• Measurable reduction in manual operational effort driven by AI automation.

• Deployment of enterprise-scale behavioral analytics or risk scoring systems.

• Recognized as the technical backbone of Cyber AI Engineering delivery.

Required Skills:

Accountability, AI Architecture, Applied Engineering, Communication, Computer Science, Cybersecurity Analytics, Data Engineering, Data Science, Design Applications, Information Security, Machine Learning (ML), Mechatronics, Operational Resilience, Operations Research, Pla