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Popular AI Engineer title, metro location, and mid-level experience increase competition, while niche agentic LLM skills narrow the pool.
Core LLM engineering skills are transferable, but security-specific agent experience increases domain specificity.
Mandatory 2+ years plus specific LLM, RAG, vector DB, and production deployment experience enforces strict filters.
Design, build, and deploy autonomous AI agents and multi-agent systems for security detection and response at scale.
Optimize agentic pipelines for latency, accuracy, operational stability, cost-efficiency, and tool-use fidelity in production environments.
Collaborate with security researchers and engineers to transform detection workflows into automated, agent-powered solutions and continuously improve agent performance and robustness.
Minimum 2+ years experience in AI Engineering, Applied AI, or Machine Learning Engineering.
Strong Python engineering skills and experience with LLM APIs (OpenAI, Anthropic, Gemini) and open-source models (Hugging Face or similar).
Experience designing multi-step, tool-using agents with orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI) and deploying agentic systems in cloud or containerized environments.
Work Experience Required: Minimum 2+ years in relevant AI/ML engineering roles.
Experienced in architecting multi-agent, tool-using AI systems optimized for real-world security applications and production-grade reliability.
Demonstrated ability to implement retrieval-augmented generation systems and vector stores for semantic search and data retrieval.
Comfortable working directly with security domain experts to translate complex workflows into automated AI agent pipelines within operational security environments.