Security Engineer, AI Platform Engineering

Saronic Saronic · Defense · Austin, TX · Software

Security Engineer focused on building and enabling secure AI platforms and applications for internal teams. This role involves building AI-powered applications and agents, setting standards for AI usage, implementing governance and guardrails, and educating colleagues on safe and effective AI adoption. The emphasis is on building robust, secure, and production-ready AI solutions rather than just demos.

What you'd actually do

  1. Build AI-powered applications, agents, and automations for teams across the company on our cloud platforms, with real backends, infrastructure, and integrations to real data and systems, for properly hardened, compliant, well-hosted, secure-by-default solutions, so departments don’t ship insecure ad-hoc vibe-coded tools themselves.
  2. Teach teams to use AI safely and effectively for their own work, and set company-wide standards for good, safe AI usage.
  3. Own AI governance, visibility, and inventory; monitoring and logging of AI usage; and prompt- and output-level data-loss-prevention to protect sensitive data, including customer data.
  4. Put guardrails in place for AI usage, treat AI agents as identities with least privilege, govern model and agent access, and make the sanctioned path the best path so “shadow AI” doesn’t take hold.
  5. Build agents and workflows that actually work, design tool use and MCP integrations, manage context and memory, and validate quality with evaluative loops.

Skills

Required

  • AI power user who builds agents, tools, or automations with LLMs
  • Build real backends and infrastructure (APIs, data pipelines, authentication, hosting, deployment, systems integration)
  • Modern agent concepts (agentic loops, context engineering, tool use/function calling, MCP)
  • Understanding of AI model strengths and weaknesses
  • Software and security foundation for production systems
  • Data handling and safe AI usage
  • Supporting and teaching non-technical users
  • Ability to obtain and maintain a U.S. security clearance

Nice to have

  • AI governance
  • DLP
  • monitoring/logging
  • guardrails

What the JD emphasized

  • build real backends and infrastructure, not just demos
  • properly hardened, compliant, well-hosted, secure-by-default solutions
  • AI governance
  • security guardrails
  • prompt- and output-level data-loss-prevention
  • treat AI agents as identities with least privilege
  • validate quality with evaluative loops

Other signals

  • AI governance
  • security guardrails
  • AI platform enablement
  • building AI agents and automations
  • customer-facing enablement