AI and Data Team Manager

Essential Duties and Responsibilities

• Lead Technical Strategy & Execution: Define and drive the technical roadmap for AI, ML, and data systems, overseeing development, deployment, and operationalization to ensure robust performance, scalability, and direct alignment with key business strategy and requirements.
• Team Leadership & Mentorship: Lead, mentor, and grow a small high-performing team of data scientists and ML engineers, cultivating a culture of technical excellence, accountability, and continuous learning.
• AI/ML/Generative Systems Development: Direct the entire lifecycle—from design to deployment—of robust data pipelines, scalable model training, and innovative AI/ML applications. Focus on leveraging these systems to significantly boost analyst and hacker productivity across critical offensive security use cases.
• Model Development and Platform Integration: Guide the development, tuning, deployment, and MLOps of Machine Learning models for cybersecurity. Ensure secure and compliant integration of cutting-edge generative AI models (via platforms like AWS Bedrock, OpenAI, Anthropic) with internal APIs and sensitive security datasets.
• Data Architecture Governance: Architect, govern, and optimize large-scale, high-performance data pipelines essential for securely and efficiently processing massive vulnerability, asset, and activity datasets sourced from various environments.
• Security & Scalability Partnership: Collaborate closely with infrastructure teams to architect AI workloads and data pipelines that meet stringent requirements for security, efficiency, and scalability, particularly within multi-tenant and regulated environments (e.g., FedRAMP, SOC2).
• Cross-Functional Collaboration: Serve as the primary technical subject matter expert and liaison, partnering with security research, product, and platform teams to translate complex offensive security challenges into robust, data-driven automation and intelligence solutions.
• Continuous MLOps Improvement: Establish and manage best-in-class MLOps practices.