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Niche senior ML/AI role reduces applicants, but strong AI demand and metro location keep competition medium.
Requires deep LLM/MLOps expertise and cybersecurity domain knowledge, limiting cross-industry transferability.
Explicit 12+ years, 5+ years ML production experience and mandatory LLM/MLOps/security skills make filtering strict.
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Define and architect the AI system runtime, including agentic workflows, Retrieval-Augmented Generation systems, and multi-modal reasoning for cybersecurity data.
Lead modeling and fine-tuning strategies for domain-specific large language and specialized models interpreting network and security telemetry.
Design and implement AI trust, alignment, safety frameworks, and runtime guardrails for safe, deterministic AI in mission-critical security operations.
12+ years in software and systems engineering with 5+ years leading complex machine learning, NLP, or deep learning systems in enterprise production.
Expertise in generative AI and large language models, including model training, RLHF/DPO alignment, prompt engineering, and orchestration frameworks.
Strong engineering knowledge of distributed training/inference infrastructure, model optimization (quantization, pruning), and cloud ML platforms like AWS Vertex/SageMaker or Kubernetes.
Understanding of cybersecurity concepts and security telemetry; ability to quickly master domain security contexts.
Senior engineer comfortable with end-to-end AI architecture and productionizing cutting-edge AI research into software.
Ability to operate autonomously in lean teams, focusing on foundational code development with strong engineering discipline.
Experience working at the intersection of AI/ML and cybersecurity, with a focus on enabling safe, auditable AI outputs in high-stakes environments.