AI Security Solutions Engineer

F5 F5 · Enterprise · Field-DC, Field-SC, Field-NC, Field-FL

Solutions Engineer focused on F5’s AI Runtime Security portfolio, working with advanced security models, inference-layer guardrails, and red-team/defend capabilities. The role supports customer adoption of AI securely and responsibly, articulating technical and business value, building demonstrations, and serving as a trusted advisor on secure AI deployment patterns.

What you'd actually do

  1. Understand customer environments and AI adoption roadmaps
  2. Articulate the technical and business value of F5’s AI security solutions
  3. Build and deliver compelling demonstrations of inference-layer protection, model red-teaming, and agent defense
  4. Serve as a trusted advisor on secure AI deployment patterns
  5. You’ll collaborate closely with Engineering, Product, Sales, and Security teams to drive customer success, influence roadmap priorities, and ensure customers can confidently scale their AI initiatives.

Skills

Required

  • 5+ years in a customer-facing technical role (Solutions Engineer, Sales Engineer, Security Engineer, or similar)
  • Strong understanding of AI, LLMs, Security, Cloud, and SaaS technologies, especially in Generative AI
  • Excellent communication skills and the ability to clearly articulate complex technical concepts
  • Experience collaborating across engineering, product, and sales organizations

Nice to have

  • Strong problem-solving and strategic thinking
  • High empathy and the ability to understand customer challenges
  • Self-directed, proactive, and able to manage multiple priorities
  • Passion for AI technologies and eagerness to master emerging threats, patterns, and guardrail techniques

What the JD emphasized

  • AI Runtime Security
  • inference-layer guardrails
  • red-team/defend capabilities
  • securing AI inference
  • agentic systems
  • enterprise LLM deployments
  • AI security solutions
  • inference-layer protection
  • model red-teaming
  • agent defense
  • secure AI deployment patterns
  • AI red-team methods
  • inference attacks
  • model-specific vulnerabilities

Other signals

  • securing AI inference
  • agentic systems
  • enterprise LLM deployments
  • customer adoption of AI securely and responsibly