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# AI Security Engineer

- **Company:** SMBC, L.C.
- **Location:** Charlotte, NC, United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Active Directory, Artificial Intelligence, Amazon Web Services, User Authentication, Microsoft Azure, Cloud Computing, Cloud Computing Security, Cyber Security, Information Leak Prevention, Multi-Factor Authentication, Identity and Access Management, Intrusion Detection and Prevention, Machine Learning, Open Web Application Security, Azure Active Directory, Red Team (Cyber Security), Software Engineering, Web Applications, SSL Certificate Management, Data Logging, Google Cloud, Cloud Platform System, Data Ingestion, Large Language Models, Software Security, Generative AI, Containerization, AI Platforms, Information Technology, Machine Learning Operations, Virtual Agents, Devsecops, Databricks
- **Published:** August 29, 2026
- **Apply:** https://www.beyondcharlotte.com/job.asp?id=3369496383&tx=JL9993FFR&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Bachelor's degree in Information Security, Computer Science, Engineering, or a related field, or an equivalent combination of education, certifications, and experience.
 * 3 to 5 years of experience in cybersecurity, security engineering, cloud security, or security architecture.
 * Experience designing and implementing security solutions in cloud environments such as Azure, AWS, or Google Cloud Platform (GCP).
 * Knowledge of enterprise security concepts, including networking, operating systems, web applications, identity and access management, authentication, privileged access controls, and certificate management.
 * Understanding of application security, API security, modern threat techniques, and cloud security risks.
 * Familiarity with security and compliance frameworks such as NIST, OWASP, ISO, SOX, and SWIFT.
 * Working knowledge of AI, machine learning, and generative AI technologies, including security considerations throughout the AI lifecycle.
 * Strong written and verbal communication skills with the ability to explain technical concepts to both technical and non-technical audiences.

Cloud Platforms

 * Microsoft Azure
 * Amazon Web Services (AWS)
 * Google Cloud Platform (GCP)

Identity & Access Management

 * Active Directory
 * Microsoft Entra ID (Azure Active Directory)
 * Multi-factor authentication (MFA)
 * Identity governance and privileged access management, * Hands-on experience with Azure OpenAI, AWS Bedrock, Databricks AI, or similar AI platforms.
 * Experience implementing security controls within MLOps environments.
 * Exposure to AI security testing, red teaming, or adversarial attack simulations.
 * Security certifications such as CISSP, GSEC, GIAC, CEH, CSSLP, CCSP, or cloud security certifications.
 * Advanced degree in Information Security, Cybersecurity, Computer Science, or a related discipline.

## Description

As an AI Security Engineer, you will help secure the organization's AI, machine learning, generative AI, and agentic AI platforms. You will work with security, cloud, engineering, data, and risk teams to build security controls that protect AI systems throughout their lifecycle. This role focuses on identifying and reducing AI-specific risks while enabling the safe, compliant, and responsible adoption of AI technologies across the enterprise., * Design, implement, and maintain security controls for generative AI, large language models (LLMs), AI agents, and machine learning platforms.

 * Develop secure architecture patterns, standards, and guardrails for AI solutions across data ingestion, model development, deployment, and inference workflows.
 * Integrate AI security requirements into software development, DevSecOps, and MLOps processes.
 * Secure AI platforms and services, including Azure AI, Azure OpenAI, AWS Bedrock, Databricks AI, and related cloud-native technologies.
 * Monitor AI environments by implementing logging, telemetry collection, threat detection, and security monitoring capabilities.
 * Assess AI solutions for risks such as prompt injection, model poisoning, data leakage, adversarial attacks, unauthorized access, insecure agent behavior, retrieval-augmented generation (RAG) vulnerabilities, and third-party supply chain threats.
 * Partner with architecture, engineering, data science, privacy, legal, compliance, and cloud teams to incorporate security requirements into solution design, deployment, and operations.
 * Support AI security testing, red team exercises, incident response preparedness, control validation, and continuous improvement initiatives.


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