Solutions Engineer — AI & Data Science Specialist

F5 F5 · Enterprise · Seattle, WA

Solutions Engineer specializing in AI Runtime Security, focusing on interpreting and explaining AI security testing results (POCs, red-teaming, guardrail evaluations) to customers and internal teams. The role bridges customer-facing solution engineering with internal data science, diagnosing issues like false positives/negatives, defining risk thresholds, and refining prompts/policies. It involves translating complex AI behavior into business-relevant narratives and acting as an escalation point for AI behavior questions.

What you'd actually do

  1. Analyze and interpret results from AI Runtime Security POCs, including red-team campaigns, prompt/response scans, and inference-layer inspections.
  2. Diagnose false positives and false negatives, explaining root causes in clear, customer-friendly language.
  3. Help define acceptable risk thresholds and success criteria for enterprise AI security deployments.
  4. Partner with customers to refine prompts, policies, scanner descriptions, and evaluation strategies.
  5. Act as the escalation point for complex AI behavior questions during evaluations and pilots.

Skills

Required

  • Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, AI, or a related technical field.
  • 5+ years of experience in a technical, customer-facing role (Solutions Engineer, ML Engineer, Data Scientist, Applied AI Engineer, or similar).
  • Strong understanding of Large Language Models (LLMs)
  • Strong understanding of Prompt engineering and prompt evaluation
  • Strong understanding of Model behavior, bias, and limitations
  • Strong understanding of False positive / false negative tradeoffs in ML systems
  • Experience analyzing model outputs, classification results, or evaluation metrics.
  • Ability to explain complex AI/ML concepts clearly to non-data-scientists.

Nice to have

  • Hands-on experience with prompt engineering, LLM evaluation, or model testing.
  • Familiarity with AI security concepts such as: Prompt injection, Jailbreaks, Data leakage, Model misuse and abuse patterns
  • Experience working with real customer datasets or evaluation pipelines.
  • Comfort working with Python, notebooks, or lightweight analysis tooling (even if not production-focused).

What the JD emphasized

  • critical gap
  • interpret, analyze, and explain AI security testing results
  • AI/ML subject-matter expert
  • false positives and false negatives
  • model behavior
  • acceptable risk thresholds
  • evaluation strategies
  • complex AI behavior questions
  • prompt evaluation
  • model behavior, bias, and limitations
  • False positive / false negative tradeoffs
  • analyzing model outputs
  • prompt engineering, LLM evaluation, or model testing
  • AI security concepts
  • real customer datasets or evaluation pipelines
  • explaining why models behave the way they do
  • living in the gray areas of AI
  • evaluating, trust, and secure AI systems at runtime

Other signals

  • AI Runtime Security
  • LLM behavior
  • customer-facing
  • evaluating AI security testing results
  • false positives and false negatives