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Senior, niche ML/AI-with-security role reduces applicant pool despite metro location.
Strong dependency on ML production expertise and cybersecurity domain makes backgrounds less transferable.
Explicit 12+ years and mandatory ML plus cybersecurity domain experience increases filtering strictness.
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Own the architecture and implementation of the AI runtime system for next-gen cybersecurity platform, including agentic workflows and multi-modal reasoning engines.
Lead AI modeling strategy, including domain-specific fine-tuning of models and embedding techniques for network security data interpretation.
Design and enforce AI trust, safety, and alignment frameworks to ensure deterministic, audit-ready AI outputs in mission-critical security contexts.
12+ years software and systems engineering experience with 5+ years leading/deploying complex ML, NLP or deep learning systems in enterprise production.
Proven expertise in generative AI and large language model (LLM) architectures, model training, alignment techniques (RLHF/DPO), and orchestration frameworks.
Strong knowledge of distributed training/inference infrastructure and cloud ML platforms (AWS Vertex/SageMaker, Kubernetes, GPU compute stacks).
Cybersecurity domain understanding or rapid mastery capability of security telemetry, log formats, and threat intelligence.
Experienced in pioneering AI system design with strategic ownership of AI runtime and modeling for large-scale security data.
Comfortable working autonomously in a lean team while delivering production-grade, high-business-value AI capabilities safely and pragmatically.
Demonstrated ability to translate cutting-edge AI research into robust production software, with deep integration into cybersecurity applications and data architectures.