Senior AI Engineer, Security AI

Microsoft Microsoft · Big Tech · Redmond, WA +2 · Software Engineering

Senior AI Engineer focused on building AI-powered security systems for Microsoft's cloud environment. The role involves designing, developing, and evaluating AI capabilities using LLMs, agentic workflows, RAG, knowledge graphs, and multi-modal signals. Key responsibilities include translating security challenges into AI problems, developing reasoning approaches, designing agent architectures, defining evaluation methodologies, and ensuring responsible AI practices. The role operates at the intersection of agentic systems, applied AI, cloud security, and responsible AI.

What you'd actually do

  1. Design, implement, and advance AI approaches using large language models, agentic workflows, retrieval-augmented generation, knowledge graphs, feedback-driven optimization, multi-modal security signals, and rigorous evaluation frameworks to support security engineering and security operations workflows.
  2. Translate open-ended security challenges into AI problems, formulate hypotheses, design experiments, and develop measurable success criteria.
  3. Develop innovative approaches for reasoning over complex security information, synthesizing context, identifying patterns, and generating actionable insights.
  4. Design and improve agent architectures, planning and execution strategies, tool-use frameworks, memory systems, retrieval techniques, and grounding mechanisms for security applications.
  5. Define evaluation methodologies, benchmarks, datasets, metrics, and testing frameworks to measure quality, accuracy, safety, robustness, trustworthiness, and real-world impact.

Skills

Required

  • Bachelor's Degree in Computer Science or related technical field
  • 4+ years technical engineering experience
  • coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python

Nice to have

  • Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Computational Linguistics, Mathematics, or a related field.
  • Experience designing experiments, evaluating performance of AI models and systems, defining metrics, and applying scientific methods to improve AI system quality.
  • Experience with large language models, agentic workflows, retrieval-augmented generation, knowledge graphs, feedback-driven optimization, multi-modal security signals, and rigorous evaluation frameworks.
  • Strong analytical and problem-solving skills
  • Strong communication skills
  • Experience developing AI agents, copilots, autonomous workflows, tool-using systems, planning systems, or multi-agent architectures.
  • Experience with LLM evaluation, AI safety, adversarial testing, hallucination mitigation, prompt engineering, prompt injection defense, and responsible AI practices.
  • Experience with embedding models, vector search, retrieval systems, ranking algorithms, knowledge graphs, memory architectures, and context-grounding techniques.

What the JD emphasized

  • rigorous evaluation frameworks
  • agentic workflows
  • retrieval-augmented generation
  • multi-modal security signals
  • responsible AI

Other signals

  • building AI-powered security systems
  • agentic workflows
  • retrieval-augmented generation
  • rigorous evaluation frameworks
  • multi-modal security signals