AI Security & Compliance Engineer (AI/ML Security, Cloud Security, DevSecOps)

We re looking for a Security Engineer to make sure AI and GenAI systems are designed, deployed, and run securely and in line with enterprise cybersecurity, privacy, and regulatory standards. You ll cover new LLM risks alongside AWS cloud, application, data security, DevSecOps, and infrastructure-as-code controls, and build reusable controls that work across multiple business AI use cases., * Design and review secure architectures for AI/ML platforms, LLM applications, RAG pipelines, model-serving environments, and agentic AI workflows

• Run threat modeling for prompt injection, jailbreaks, insecure tool use, model inversion, data leakage, retrieval poisoning, and third-party model risk
• Implement AWS controls: IAM, encryption, key and secrets management, network segmentation, API security, logging, and data-loss prevention
• Build security into MLOps/LLMOps, CI/CD, container security, Terraform modules, and deployment pipelines
• Review IaC templates and AWS deployments for least privilege, secure defaults, segregation of duties, and auditability
• Assess third-party models, APIs, open-source packages, and vendor platforms for security, privacy, and supply-chain risk
• Build monitoring and alerting for suspicious AI usage, anomalous access, policy violations, and data leakage
• Support AI red teaming, penetration testing, vulnerability management, incident response, and production-readiness reviews
• Maintain audit-ready documentation for controls, testing, risk acceptance, and production approvals

• Strong background in cybersecurity, cloud security, application security, DevSecOps, or technology risk
• Experience securing cloud-native platforms, APIs, microservices, containers, Kubernetes, CI/CD pipelines, and infrastructure-as-code
• Strong AWS security depth (IAM, encryption, network controls, logging, secrets, secure deployment patterns), or equivalent on another hyperscaler
• Working knowledge of AI/ML and GenAI risks: prompt injection, adversarial attacks, data leakage, model misuse, retrieval poisoning, model supply-chain risk, and unsafe tool use
• Familiarity with threat modeling, vulnerability management, security testing, incident response, secure SDLC, and Terraform/IaC controls
• Ability to work directly with engineering teams on practical, risk-based controls

Preferred

• Experience securing AI/ML platforms or GenAI applications in production
• Financial-services security, technology risk, regulatory, privacy, or audit background
• AI red teaming, secure RAG design, LLM gateways, privacy-by-design controls
• Power Platform and Copilot Studio governance, data-loss prevention, and connector controls