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.