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What You’ll Do:

  • Secure Agentic AI systems by designing controls for autonomous agents, tool execution, and memory.
  • Protect AI models and knowledge systems, including RAG pipelines and vector databases, from unauthorized access and data leakage.
  • Perform AI threat modeling to identify and mitigate risks such as prompt injection, jailbreaking, and adversarial inputs.

What We’re Looking For:

  • 7+ years of software engineering, cybersecurity, or AI engineering experience with cloud-native distributed systems.
  • Strong knowledge of LLMs, AI-specific security risks, and cloud security across AWS, Azure, or Google Cloud.
  • Proficiency in Python or TypeScript and a mindset blending security engineering with software engineering.

Nice to Have:

  • Experience securing RAG systems and using frameworks like LangGraph, LangChain, or CrewAI.
  • Familiarity with vector databases (Pinecone, Weaviate, Qdrant) and AI observability solutions.
  • Knowledge of NIST AI RMF, Zero Trust Architecture, or federal government/healthcare environments.

LTS

LTS builds an AI-native engineering platform to modernize mission-critical healthcare systems for federal agencies, serving millions of Veterans. Their culture emphasizes innovation, collaboration, and secure, transparent AI development.

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