AI-Driven Cybersecurity Scanning Engineer

Job Overview

We are looking for an experienced AI-Driven Cybersecurity Scanning Engineer with expertise in securing AI platforms, Large Language Models (LLMs), Generative AI agents, and Model Context Protocol (MCP) environments.

The ideal candidate will have hands-on experience in AI security, adversarial testing, cloud security, MLOps security, and DevSecOps. The candidate should be capable of identifying and mitigating security vulnerabilities in AI-driven applications, implementing security guardrails, and securing multi-agent architectures.

Key Responsibilities

• Design and implement AI security guardrails, safety classifiers, and security controls for GenAI applications.
• Perform adversarial testing, penetration testing, and red-team assessments of LLMs and AI agents.
• Evaluate AI security risks using OWASP Agentic Top 10, OWASP LLM Top 10, and MITRE ATLAS frameworks.
• Implement security controls for AI/ML pipelines and MLOps environments.
• Ensure model provenance, model integrity, and training-data integrity.
• Implement identity and access management using SPIFFE/SPIRE.
• Configure policy-based authorization using Open Policy Agent (OPA).
• Establish trust and security controls across multi-agent AI architectures.
• Secure MCP servers, AI integrations, and agent communication workflows.
• Integrate security testing and vulnerability scanning into CI/CD pipelines.
• Collaborate with AI/ML, cloud, and DevSecOps teams to strengthen overall security posture.

Required Technical Skills

• AI Security and Generative AI Security
• Large Language Models (LLMs) and GenAI Agents
• Model Context Protocol (MCP)
• AI Security Guardrails and Safety Classifiers
• Adversarial Testing and AI Red Teaming
• OWASP LLM Top 10 and OWASP Agentic Top 10
• MITRE ATLAS Framework
• MLOps Security
• Model Provenance and Training-Data Integrity
• SPIFFE/SPIRE Identity Management
• Open Policy Agent (OPA)
• Multi-Agent Security and Trust Controls
• Cloud Security and DevSecOps
• Secure SDLC and CI/CD Security Integration

Hands-on Experience: Candidates should ideally have practical experience in:

• Building and securing Generative AI agents.
• Implementing and securing MCP servers.
• Deploying and tuning open-weight AI models.
• Using cybersecurity tools for common security functions.
• Identifying and mitigating vulnerabilities in AI-powered applications.
• Implementing security controls across AI platforms and cloud environments.

Preferred Qualifications

• AWS Security Specialty certification or equivalent cloud security certification.
• CISSP or CCSP certification.
• Experience with enterprise AI security platforms and LLM security assessments.
• Knowledge of emerging AI security standards and threat frameworks.

Experience Requirements

• Overall experience: 4–6 years as specified for the position.
• Advanced AI/GenAI and cybersecurity experience should align with the technical requirements.
• Hands-on implementation experience is preferred over theoretical knowledge.