AI Security Engineer

Summary: • The project delivers an enterprise AI Guardrails capability to provide centralized, consistent, and auditable runtime protection for AI-enabled applications. • Deployment of AI Guardrails by leveraging AWS EKS to manage the control plane while hosting the inference data plane on GPU enabled compute for high-performance model execution. • The solution will include a unified administration console and a multi-AZ, high-availability deployment architecture. Key deliverables encompass the deployment topology, governance framework, and foundational platform setup with native integration with AI Gateway to support enterprise-scale operations. Skillset Requirements: • Minimum 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, AI application security, or AI/ML security within an enterprise environment. • Hands-on experience securing AI systems and Large Language Models (LLMs), including GPT, Claude, Llama, Gemini, or equivalent models. • Strong understanding of AI security risks and controls, including prompt injection, data leakage, model poisoning, insecure output handling, model theft, supply chain risks, and agentic AI threats. • Hands-on experience with AI Security Guardrails, AI Red Teaming, AI Model Scanning, AI risk assessments, and threat modeling. • Hands-on experience designing, developing, assessing, or securing Agentic AI systems 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 and scripting skills in Python and at least one additional language such as Java, Go, C#, or C++. • Strong understanding of REST APIs, JSON, authentication protocols, and API security. • Experience in conducting security assessments, threat modeling, security architecture reviews, and secure design reviews. • Experience with DevSecOps practices, CI/CD pipelines, Infrastructure as Code (IaC), GitHub/GitLab, automated security testing, and cloud-native deployments. • Experience with container and orchestration technologies such as Docker and Kubernetes. • Knowledge of SAST, DAST, SCA, secrets management, software supply chain security, and secure software development practices. • Ability to produce architecture designs, security assessments, technical documentation, and process flows. • Strong stakeholder management, communication, collaboration, and problem-solving skills. • Possess positive learning and collaborative mindset. • Good written and verbal communication skills. • Agile, fast learner and able to adapt to changes.