AI Security Engineer – AI Guardrails / LLM Security

Role Overview We are looking for an experienced AI Security Engineer to design, implement, and secure an enterprise-wide AI Guardrails platform supporting AI-enabled applications. The role will focus on delivering centralised, consistent, and auditable AI security controls, leveraging AWS EKS, GPU-enabled infrastructure, and AI Gateway integration. The platform will support high-performance model inference, unified administration, and a multi-Availability Zone (Multi-AZ), high-availability architecture. Key Responsibilities • Design, develop, and implement AI Security Guardrails to protect enterprise AI applications and Large Language Models (LLMs). • Deploy and manage AI security infrastructure using AWS EKS, Kubernetes, and GPU-enabled compute environments. • Establish secure deployment architectures, governance controls, and integration with AI Gateway platforms. • Conduct AI security assessments, threat modelling, architecture reviews, and AI Red Teaming. • Identify and mitigate AI-specific threats, including prompt injection, data leakage, model poisoning, insecure outputs, model theft, supply chain vulnerabilities, and Agentic AI risks. • Assess and secure Agentic AI systems, Model Context Protocol (MCP) integrations, and LLM applications. • Implement AI model scanning, automated security testing, and runtime security controls. • Develop security automation and integrations using Python, REST APIs, and cloud-native technologies. • Integrate security controls into DevSecOps, CI/CD pipelines, and Infrastructure as Code (IaC) workflows. • Produce technical documentation, security assessments, architecture designs, and operational procedures. • Collaborate with engineering, cloud, cybersecurity, and AI teams to ensure secure and scalable platform delivery. Must-Have Requirements • Degree in Computer Science, Information Technology, or a related discipline. • At least 6 years of experience in Cybersecurity, Application Security, Cloud Security, Platform Security, or Secure Software Engineering. • Minimum 2 years of hands-on experience in AI Security, Generative AI Security, LLM Security, or AI/ML Security within an enterprise environment. • Experience securing LLMs and AI applications, including models such as GPT, Claude, Llama, or Gemini. • Strong knowledge of AI security threats, AI Guardrails, AI Red Teaming, model scanning, risk assessments, and threat modelling. • Experience securing Agentic AI applications and Model Context Protocol (MCP) ecosystems. • Experience with cloud AI platforms such as AWS Bedrock, AWS SageMaker, Azure AI Foundry/Azure ML, or Google Vertex AI. • Strong programming skills in Python and at least one additional language: Java, Go, C#, or C++. • Good understanding of REST APIs, JSON, authentication protocols, and API security. • Experience with DevSecOps, CI/CD, Infrastructure as Code (IaC), GitHub/GitLab, and automated security testing. • Hands-on experience with Docker, Kubernetes, and cloud-native deployments. • Knowledge of SAST, DAST, SCA, secrets management, software supply chain security, and secure development practices. • Strong analytical, troubleshooting, documentation, communication, and stakeholder management skills. • Experience in banking, financial services, asset management, or regulated environments. • Experience with Databricks and Databricks AI Security Framework (DASF 2.0). • Knowledge of OWASP Top 10 for LLM Applications, OWASP Agentic AI Security, NIST AI RMF, MITRE ATLAS, and CSA AI Controls Framework. • Exposure to AI security platforms such as Operant AI, HiddenLayer, Protect AI, Prompt Security, Palo Alto Prisma AIRS, or NVIDIA NeMo Guardrails. • Experience with AI Gateway, AI observability, AI governance, model risk management, and LLM evaluation frameworks. • Familiarity with RAG architectures, vector databases, MLflow, Snowflake, and Databricks. • Experience working in Agile, Scrum, DevOps, MLOps, or SAFe environments. • Certifications such as CISSP, CISM, CCSP, CCSK, CEH, GIAC, or relevant Cloud/AI Security certifications. Key Technologies AI Guardrails | LLM Security | Agentic AI | MCP | Python | AWS EKS | Kubernetes | Docker | AWS Bedrock | SageMaker | Azure AI Foundry | Vertex AI | AI Gateway | REST APIs | DevSecOps | CI/CD | IaC | GitHub/GitLab | SAST | DAST | SCA | AI Red Teaming | NVIDIA NeMo Guardrails