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Senior, highly specialized AI/ML role with cybersecurity focus at a non-Tier1 employer reduces applicant density.
Requires deep ML/LLM production experience and cybersecurity domain knowledge, lowering cross-industry transferability.
Explicit 12+ years and 5+ years ML production experience plus mandatory generative AI, MLOps, and cybersecurity expertise.
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Architect and define the AI system runtime, including complex workflows, Retrieval-Augmented Generation systems, and multi-modal reasoning engines for cybersecurity data.
Lead modeling and fine-tuning strategies for domain-specific AI models to interpret network behavior, security schemas, and configurations.
Design and implement AI trust, alignment, safety frameworks, and runtime guardrails to ensure safe, deterministic, and audit-ready AI for security operations.
12+ years software and systems engineering experience, including 5+ years leading and deploying complex machine learning, NLP, or deep learning systems in enterprise production.
Proven expertise in generative AI and LLMs, including model training, alignment techniques (RLHF/DPO), prompt compilation, and orchestration frameworks.
Strong understanding of distributed training/inference infrastructure, model optimization (quantization, pruning), and cloud ML platforms such as AWS Vertex/SageMaker and Kubernetes.
Cybersecurity domain knowledge or demonstrated ability to quickly master security-specific contexts; Work Experience Required: 12+ years with 5+ in ML/NLP/AI deployments.
Experienced technical leader able to define scalable AI architectures for complex security telemetry and unstructured data analysis.
Expert in productionizing generative AI at enterprise scale with a focus on safety, trust, and compliance for mission-critical cybersecurity.
Able to thrive in lean, autonomous teams by writing foundational production-grade AI software and collaborating closely with data engineering counterparts.