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Strong employer brand and metro location with mid-level experience, but specialized LLM+security focus limits generalist competition.
Requires specialized AI-for-security, Microsoft security platform, and enterprise integration experience limiting cross-industry transferability.
Explicit years plus mandatory LLM, security platform, and enterprise integration experience create stringent shortlisting filters.
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Design, build, and deploy production-grade AI systems for autonomous cyber defense and enterprise security optimization integrated with Microsoft Defender XDR, Sentinel, and related platforms.
Develop and engineer LLM-based agents with contextual reasoning, summarization, classification, prompt strategies, and structured evaluation frameworks to improve operational workflows and risk scoring.
Implement safety mechanisms such as guardrails and rollback, partner with Data Engineering to develop production datasets, and establish monitoring/feedback loops for AI model performance.
3–6+ years of hands-on AI/ML engineering experience.
Strong proficiency in Python and modern ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow).
Experience deploying production AI systems integrated into enterprise platforms or APIs; demonstrated work on LLM-based systems and prompt engineering.
Familiarity with Microsoft Defender XDR, Sentinel, KQL, and AI orchestration tools such as Microsoft Copilot Studio.
Experienced AI engineer skilled in developing multi-agent orchestration systems and autonomous security workflows within enterprise cybersecurity platforms.
Proficient in translating ambiguous operational problems into structured AI-driven automation and decision-making solutions for cybersecurity.
Capable of delivering measurable efficiency and risk mitigation improvements integrated directly into regulated security enforcement platforms.