AI Security & Compliance Engineer

AI Security & Compliance Engineer Location: Jersey City, NJ (Hybrid – 4 Days Onsite) AI/ML Security / GenAI Risk / Cloud Security / DevSecOps The AI Security & Compliance Engineer will design, implement, and enforce security and compliance controls for AI, Machine Learning (ML), and Generative AI (GenAI) solutions across the AIRP platform. This role ensures AI systems are securely designed, deployed, and operated in compliance with enterprise cybersecurity, cloud security, privacy, regulatory, and technology governance standards. The position combines expertise in AI/ML security, LLM security, AWS cloud security, DevSecOps, Infrastructure-as-Code (IaC), application security, and compliance to protect enterprise AI workloads from emerging threats while maintaining secure, scalable, and audit-ready environments. The organization is building a secure, cloud-agnostic AI platform, and this role will establish reusable security controls that protect AWS-hosted AIRP environments, Terraform/IaC templates, CI/CD pipelines, cloud-native architectures, AI applications, LLM-powered solutions, and model/data access controls. The engineer will also contribute to governance for Microsoft Power Platform, Copilot Studio, Power Apps, Power Automate, including Data Loss Prevention (DLP), connector governance, and citizen-development security controls. The successful candidate will own security architecture, AI threat modeling, control implementation, security testing, AI red teaming, vulnerability management, compliance evidence, and production security approvals for AI platforms, LLM applications, RAG pipelines, model-serving environments, and agentic AI systems. This role partners closely with Engineering, Cloud Infrastructure, DevOps, Cybersecurity, Risk, Compliance, Privacy, and Audit teams to embed practical, risk-based security controls throughout the AI development lifecycle. Key Responsibilities • Design, implement, and review secure architectures for AI/ML platforms, Generative AI applications, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) pipelines, model-serving environments, AI agents, and agentic AI workflows. • Develop and execute AI threat models addressing prompt injection, jailbreak attacks, insecure tool usage, model inversion, adversarial ML attacks, retrieval poisoning, data leakage, model theft, unauthorized access, third-party model risks, and AI supply-chain vulnerabilities. • Implement enterprise security controls for AWS IAM, encryption, AWS KMS, Secrets Manager, network segmentation, API security, logging, monitoring, secure data handling, privacy controls, and Data Loss Prevention (DLP). • Embed security throughout the MLOps, LLMOps, DevSecOps, CI/CD pipelines, container platforms, Kubernetes environments, Infrastructure-as-Code (Terraform), and deployment automation to ensure secure software delivery and operational resilience. • Review and secure Terraform modules, Infrastructure-as-Code templates, AWS cloud deployments, and cloud-agnostic architectures to enforce least privilege, secure defaults, segregation of duties, policy compliance, governance standards, and auditability. • Assess third-party AI models, APIs, open-source libraries, AI frameworks, SaaS platforms, and vendor solutions for security, privacy, model supply-chain, licensing, and compliance risks before production deployment. • Build enterprise monitoring, alerting, and detection capabilities for suspicious AI usage, anomalous access patterns, prompt abuse, policy violations, unsafe model interactions, privilege escalation, and potential data leakage. • Lead AI red teaming, penetration testing, vulnerability assessments, incident response, remediation planning, production readiness reviews, and security validation for AI platforms and GenAI applications. • Maintain audit-ready security documentation, including security architecture, control evidence, compliance artifacts, penetration test reports, risk assessments, remediation tracking, production approvals, and governance documentation. • Collaborate with Engineering, Cloud Infrastructure, DevOps, Cybersecurity, Risk Management, Compliance, Privacy, Legal, and Audit teams to implement scalable security controls that protect enterprise AI workloads while enabling responsible innovation. • Support governance and security controls for Microsoft Power Platform, Copilot Studio, Power Apps, Power Automate, including connector governance, Data Loss Prevention (DLP), citizen-development oversight, and secure AI enablement. Must-Have Candidate Profile • Strong background in Cybersecurity, Cloud Security, Application Security, DevSecOps, Infrastructure Security, or Technology Risk. • Experience securing cloud-native platforms, APIs, microservices, Kubernetes, containers, CI/CD pipelines, Infrastructure-as-Code (Terraform), and enterprise cloud environments. • Strong hands-on expertise with AWS Security, including IAM, KMS, encryption, VPC security, Secrets Manager, CloudTrail, CloudWatch, logging, network controls, and secure deployment architectures. • Deep understanding of AI/ML and Generative AI security risks, including prompt injection, jailbreak attacks, adversarial ML, retrieval poisoning, model inversion, model theft, data leakage, unsafe tool usage, AI supply-chain attacks, and LLM security. • Experience with threat modeling, secure SDLC, DevSecOps, vulnerability management, penetration testing, incident response, AI red teaming, compliance controls, and Terraform/IaC security. • Ability to work closely with engineering teams to translate security requirements into practical, scalable, risk-based technical controls. Preferred Experience • Experience securing AI/ML platforms, LLM applications, RAG systems, AI agents, or Generative AI solutions in production. • Experience within Banking, Financial Services, Insurance, FinTech, Healthcare, or other highly regulated industries. • Familiarity with AI Red Teaming, Secure RAG Architecture, LLM Gateways, Power Platform Governance, Copilot Studio Security Controls, Data Loss Prevention (DLP), Privacy-by-Design, and enterprise AI governance frameworks. • Knowledge of regulatory and compliance frameworks supporting security, privacy, technology risk, audit, and AI governance.