AI Security Engineer - INTL Costa Rica

• Secure and support AI/LLM, Generative AI, and Agentic AI applications.

• Perform AI security assessments, threat modeling, and vulnerability reviews.

• Implement security controls across cloud environments, APIs, and AI infrastructure.

• Monitor and respond to security events using SIEM, EDR, XDR, and Microsoft Security tools.

• Develop and maintain AI security policies, governance frameworks, and compliance standards.

• Partner with engineering and cloud teams to embed security into AI solutions.

• Identify and mitigate AI-specific threats, including prompt injection, jailbreaking, and data poisoning., Insight Global is seeking an experienced AI Security Engineer for a leading enterprise client. This individual will play a critical role in securing the organization’s AI ecosystem, including Generative AI, Agentic AI, and LLM-based applications. The ideal candidate will combine deep cybersecurity expertise with hands-on experience securing AI technologies, cloud platforms, and enterprise environments. This role will focus on implementing AI security best practices, developing governance frameworks, identifying emerging threats, and partnering with cross-functional teams to ensure scalable, compliant, and secure AI adoption across the business.

• 5+ years of experience in Application Security, Cloud Security, Cybersecurity Engineering, AI Security, or a related field.

• Security * SIEM/EDR

• Antivirus & firewall technologies

• AI security policies and governance

• ISO 27001 / NIST frameworks

• AI agent development

• Microsoft Security suite

• Palo Alto (XSIAM/XDR)

• 2+ years of hands-on experience securing AI/LLM applications in production environments.

• Experience building, securing, assessing, or hardening Generative AI, Agentic AI, and LLM-based solutions.

• Strong understanding of AI security threats and mitigation strategies, including OWASP Top 10 for LLMs, prompt injection, jailbreaking, data poisoning, model extraction, adversarial attacks, and AI threat modeling.

• Experience securing AI platforms and cloud-based AI workloads, including MCP, agentic workflows, API security, IAM, secrets management, network isolation, logging/monitoring, and secure AI infrastructure design in Azure (preferred), AWS, GCP, or multi-cloud environments.

• Experience with Microsoft AI and security technologies, including Azure AI Services, Azure AI Foundry/AI Studio, Microsoft 365 Copilot, Microsoft Security, and enterprise identity solutions.

Experience developing and implementing AI security controls, governance frameworks, and compliance standards (NIST, ISO 27001, SOC 2, etc.), leveraging technologies such as SIEM, EDR, and enterprise identity platforms.