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Armis is an AI-driven cybersecurity company where Machine Learning and Generative AI power core capabilities in their Vulnerability Management, Detection, and Remediation (VMDR) platform. You will join as a Staff AI Engineer, acting as a technical builder who owns complex problem domains end-to-end, diving deep into cybersecurity nuances and architecting cutting-edge LLM and ML solutions from research through scalable production deployment.
Key responsibilities include:
- End-to-End Ownership: Drive complex AI and ML initiatives through the full product lifecycle from early-stage prototyping and deep domain analysis to production-grade deployment and monitoring.
- Architect & Deploy: Design and build production-ready AI models and system pipelines in Python using modern cloud and big data infrastructure (AWS, Spark).
- LLM Innovation: Integrate state-of-the-art Generative AI and LLM workflows directly into core VMDR features to drive automated detection, prioritization, and remediation insights.
- Domain Deep-Dives: Partner closely with domain researchers, data engineers, and product leaders to translate complex security problems into high-impact AI solutions.
You will work in a distributed, flexible environment with trust-based work personas. ServiceNow is the AI control tower for business reinvention, working with 85% of the Fortune 500.
QUALIFICATIONS:
- Cybersecurity Background: Domain experience in Vulnerability Management, Asset Intelligence, or Threat Detection is advantageous but not mandatory.
- LLM Expertise: At least 2 years of hands-on experience with Large Language Models, including prompt engineering, fine-tuning, RAG, agentic workflows, and orchestration frameworks (LangChain, LlamaIndex, vLLM, or similar).
- Solid ML Foundations: Deep algorithmic and theoretical knowledge in Machine Learning, supported by an M.Sc. or Ph.D. (or equivalent research experience).
- Production Engineering Skills: 5+ years of software development experience with strong, production-grade Python coding and experience deploying ML systems at scale.
- Self-Driven & Collaborative: Autodidact who takes full ownership of problem domains, stays ahead of emerging AI trends, and thrives in high-trust, collaborative environments.
