Assess the security of MCP clients, servers, tools, resources, authorization, permissions, and third-party integrations.
Build automated security tests and evaluation harnesses for GenAI and agentic AI applications.
Validate access controls, least-privilege implementation, human-approval mechanisms, secure secret handling, logging, and monitoring.
Integrate AI security testing into development and release processes.
Document security findings and provide clear, practical remediation recommendations.
Translate AI risk and governance requirements into appropriate technical controls.
Collaborate with engineering and security teams to embed security throughout the AI development lifecycle.
Stay current with emerging GenAI threats, agentic AI attack techniques, MCP security risks, and defensive practices.
Required Skills and Experience
Strong understanding of the end-to-end GenAI application lifecycle and its security risks.
Practical knowledge of LLMs, prompt design, RAG, embeddings, vector databases, data-ingestion pipelines, and guardrails.
Hands-on experience building, integrating, testing, or securing AI agents and tool-enabled workflows.
Understanding of agent planning, memory, tool execution, multi-agent workflows, and human-in-the-loop controls.
Knowledge of MCP architecture, authorization, permissions, and security considerations.
Experience conducting application security assessments, threat modeling, and security testing or red teaming.
Familiarity with current OWASP guidance for GenAI, LLM, and agentic applications.
Knowledge of secure application architecture, API security, identity and access management, secrets management, and data protection.
Strong hands-on Python scripting or development skills.
Experience working with APIs, SDKs, authentication, Git, and automated testing.
Ability to independently review, debug, test, and secure human-written and AI-generated code.
Experience with Azure AI Foundry, Azure OpenAI, or comparable enterprise AI platforms.
Working knowledge of relevant AI risk and governance frameworks, including ISO/IEC 42001, the NIST AI Risk Management Framework and its Generative AI Profile, and the EU AI Act, with the ability to translate applicable requirements into technical controls.
Strong written and verbal communication skills, including the ability to explain technical risks to business stakeholders.
Qualifications
Bachelor’s degree in Computer Science, Cybersecurity, Engineering, Information Technology, or a related discipline, or equivalent practical experience.
Five or more years of relevant experience in cybersecurity, application security, software engineering, cloud security, AI engineering, or related fields.
Demonstrated hands-on experience working with Generative AI or agentic AI solutions.
Strong analytical, problem-solving, documentation, and stakeholder-management skills.
Preferred Qualifications
Experience developing AI security testing or evaluation frameworks.
Experience with Promptfoo, DeepEval, Ragas, LangSmith, MLflow, or similar tools.
Experience implementing or assessing AI security controls in regulated industries.
Experience supporting ISO/IEC 42001, AI impact assessments, or EU AI Act compliance activities.
Familiarity with Azure security, Azure DevOps, Docker, Kubernetes, or AKS.
Knowledge of model training, fine-tuning, adversarial machine learning, or AI supply-chain security.
Relevant professional training or certification in cybersecurity, cloud security, AI security, or AI governance.
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