Prompt Engineer

F5 F5 · Enterprise · Dublin, Cork

Seeking a Prompt Engineer to design, develop, test, and optimize prompts for Large Language Models (LLMs) and emerging agentic AI systems, focusing on AI-powered platforms for security testing, adversarial challenges, and NLP innovation. The role involves prompt design, optimization, collaboration with AI Researchers and Engineering teams, and research into LLM advancements and AI security.

What you'd actually do

  1. Develop, refine, and iterate on high-performing prompts for Large Language Models (LLMs) to achieve specific goals, such as security testing, red-teaming exercises (e.g., simulating prompt injection, jailbreaking, or data elicitation), or validating AI defensive measures.
  2. Experiment with and implement advanced prompting methodologies, including few-shot prompting, chain-of-thought prompting, role-playing, and structured prompts to maximize AI interaction efficiency and effectiveness.
  3. Work closely with AI Researchers and Engineering teams to integrate effective prompts into F5's AI security platforms and other advanced ML-based systems.
  4. Stay informed about cutting-edge developments in LLMs, prompt engineering strategies, and AI security advancements, applying these learnings to improve system performance.
  5. Contribute to internal knowledge bases by documenting successful strategies, experimental results, and key insights.

Skills

Required

  • prompt design and refinement for large language models
  • LLM capabilities, limitations, common failure modes, and alignment strategies
  • few-shot prompting, structured designs, and context injection
  • analytical and critical-thinking skills
  • verbal and written communication skills
  • Artificial Intelligence
  • Natural Language Processing (NLP)

Nice to have

  • prompt engineering for AI security, red-teaming, or vulnerability assessment
  • agentic AI systems (e.g., LangChain, Auto-GPT)
  • basic scripting for automation (e.g., using Python)
  • evaluation techniques for LLM performance and safety
  • APIs for major LLM providers
  • adversarial attacks against NLP models

What the JD emphasized

  • prompt design
  • Large Language Models (LLMs)
  • few-shot prompting
  • agentic AI systems
  • AI security
  • red-teaming

Other signals

  • prompt engineering
  • LLM interaction
  • AI security
  • agentic AI systems