Principal Software Engineer - AI Safety and Security

Sourced from agenticcareers.co

Microsoft's AI Safety Platform (AISP) within the AeGIS team is hiring a hands-on Principal Software Engineer to build shared AI observability, detection, investigation, and hunting systems at the intersection of cybersecurity and generative AI. You will lead architecture and write critical production code for large-scale data and detection platforms that protect users across Microsoft's AI estate. The role partners with product engineers, applied scientists, threat hunters, and incident responders from early exploration through production operation.

• Lead design and implementation of shared platform capabilities and reusable engineering patterns
• Build company-wide AI observability and monitoring systems for detection, evaluation, threat hunting, and investigation
• Design detection infrastructure for attack signatures, anomaly detection, and model-assisted analysis
• Build and operate large-scale distributed data processing systems using Spark, Kusto, data lakes, and streaming platforms
• Drive security and privacy architecture reviews and technical partner conversations
• Review designs and code, mentor engineers and scientists, and raise the technical bar

• Bachelor's Degree in Computer Science or related technical field and 6+ years technical engineering experience, or equivalent experience
• Significant ownership of distributed data, platform, or security products
• Proficiency in C, C++, C#, Java, JavaScript, or Python
• Ability to meet Microsoft, customer, and/or government security screening requirements, including Microsoft Cloud Background Check

• 2+ years working on modern AI systems such as copilots, agent-based products, LLM applications, agent orchestration, embeddings, vector databases, RAG, or evaluations
• 2+ years big data engineering with Spark, Kusto, data lakes, batch and streaming pipelines
• Experience with alerting, triage, investigation, threat hunting, or incident response
• Experience with cloud-native distributed platforms, infrastructure as code, CI/CD, Azure Data Factory, Databricks, or Kubernetes
• Deep understanding of agentic systems, tools, memory, embeddings, and AI failure modes
• Proven mentoring and technical leadership across engineers, researchers, and product leaders