This is a hands-on role at the intersection of AI and security engineering. The ideal candidate is a builder who can develop detection capabilities, automate workflows, and respond to emerging threats in a rapidly evolving landscape
What the right Security Engineer will EnjoyAI Threat Detection & Monitoring
- Design and implement detection logic for AI-specific threats, including prompt injection, adversarial inputs, model misuse, and data poisoning.
- Monitor AI systems for anomalous behavior, misuse patterns, and indicators of compromise.
- Build and maintain detection pipelines integrated with SIEM/SOAR platform
Incident Response
- Investigate and respond to AI-related security incidents, including model manipulation, API abuse, and unauthorized AI usage
- Develop and maintain incident response runbooks tailored to AI system
- Perform forensic analysis on AI applications, models, and associated data
- Security Engineering & Automation
- Develop tools and automation to support AI security monitoring, testing, and response workflow
- Integrate security controls into AI/ML development pipelines (MLSecOps
- Evaluate third-party AI tools, APIs, and integrations for security ris
k.Research & Threat Intelligence
- Track emerging AI attack techniques, jailbreaks, and adversarial method
- Contribute to internal threat intelligence and detection use case
- Participate in red team exercises targeting AI systems and LLM-based application
Required Qualificatio
- Bachelor’s degree in a related field or equivalent experience
- 3–6 years of experience in security engineering, detection & response, or a related discipline
- Strong Python skills; familiarity with scripting or backend languages is a plus
- Hands-on experience with AI/ML systems, APIs, or LLM-based applications
- Understanding of prompt engineering concepts and LLM behavi
- Experience with SIEM platforms (e.g., Splunk, Elastic) and detection development
- Familiarity with MITRE ATT&CK and threat modeling techniques
- Strong analytical and investigative skills
- Ability to communicate technical findings clearly to diverse stakeholders
- Experience building or securing LLM-integrated applications
- Familiarity with agent frameworks (LangChain, AutoGen, CrewAI)
- Understanding of MLOps pipelines and associated security risks
- Experience with adversarial ML techniques (e.g., model extraction, evasion
- Knowledge of AI governance frameworks (e.g., NIST AI RMF, EU AI Acts)
- Experience with cloud AI platforms (AWS, Azure, GC
- Security certifications (e.g., CISSP, GCIH, GCIA) or equivalent experience
- Contributions to open-source or security research
- Build a strong understanding of the AI environment and identify key detection gaps
- Contribute to detection logic and participate in an AI-related incident or simulation.
