Generative AI Prompt Engineer

• Develop and refine advanced prompting methodologies used in cybersecurity-focused AI initiatives
• Build prompt-based frameworks that support automated security analysis and testing functions
• Design AI workflows capable of generating valuable insights related to vulnerabilities and security risks
• Evaluate the effectiveness of prompts and model configurations across multiple LLM environments
• Research new prompting strategies that improve accuracy, consistency, and security-focused outcomes
• Work closely with security teams to understand findings and determine which results require further investigation
• Support AI-assisted initiatives related to vulnerability analysis, scanning, and security research
• Help structure and organize data produced by AI systems for downstream operational use
• Rapidly prototype new capabilities and iterate based on testing results and stakeholder feedback
• Remain informed on developments within Generative AI and cybersecurity to identify future opportunities

• Experience creating advanced prompt architectures for Generative AI and Large Language Models
• Demonstrated success developing AI-enabled solutions, workflows, or prompt orchestration frameworks
• Strong understanding of modern LLM platforms and model evaluation techniques
• Cybersecurity experience involving security analysis, vulnerability identification, or defensive security practices
• Familiarity with application security concepts including DAST, SAST, and risk assessment methodologies
• Programming experience in Python, Go, .NET, or related languages
• Ability to test, measure, and improve AI performance through experimentation and iteration
• Understanding of network security concepts and common attack patterns
• Strong troubleshooting, analytical, and communication skills
• Experience collaborating within technical teams to deliver business-focused outcomes

Desired Skills & Experience

• Adversarial AI, AI red team, or AI security testing experience
• Practical exposure to frontier model evaluation and prompt optimization
• Experience supporting cyber defense or threat-hunting initiatives
• Knowledge of malware behavior, vulnerability management, and security investigations
• Experience with PostgreSQL, Elasticsearch, Cloud SQL, or comparable data platforms
• Cloud infrastructure experience
• Familiarity with Kubernetes or GKE environments
• Exposure to large-scale enterprise technology ecosystems, Applicants must be currently authorized to work in the US on a full-time basis now and in the future.

• $76.14/hr - $85.80/hr W2
• 12-Month Contract
• Hybrid Schedule (3 Days Onsite)
• Dallas, Texas
• Opportunity to work at the intersection of Artificial Intelligence and Cybersecurity
• Exposure to advanced language models, security research, and innovative technology initiatives
• Collaborative, highly technical team environment focused on experimentation and delivery

You will receive the following benefits

• Medical Insurance
• Dental Benefits
• Vision Benefits
• Paid Time Off (PTO)
• 401(k)

Job Description A large enterprise technology organization is seeking an experienced Generative AI Prompt Engineer to join a specialized team focused on applying artificial intelligence to cybersecurity challenges. This group is responsible for developing AI-powered approaches that improve security testing, vulnerability analysis, and threat identification through the use of advanced language models and emerging AI technologies. The successful candidate will bring a combination of hands-on prompt engineering expertise, software development experience, and cybersecurity knowledge. Rather than focusing on AI theory, the team is looking for individuals who have built practical solutions, experimented with model behavior, and successfully implemented AI-driven capabilities that produce measurable outcomes. This role offers the opportunity to work with modern frontier models while collaborating with engineers, researchers, and security professionals on initiatives designed to strengthen enterprise security programs and improve the effectiveness of automated analysis.