AI Security Engineer
Role: AI Security Engineer Location: Remote Role Overview An experienced AI Security Engineer will help secure enterprise AI adoption across frontier model integrations, generative AI applications, AI agents, and traditional technology environments. This role sits at the intersection of AI security, cloud security, security architecture, and automation. The ideal candidate will assess and reduce risks such as prompt injection, data leakage, model misuse, insecure agent behavior, cloud misconfigurations, and unauthorized access to sensitive data. The role will also focus on using AI to strengthen cybersecurity, including vulnerability discovery, secure code review, threat modeling, security testing, detection improvement, and remediation automation.
Key Responsibilities
Assess security risks across LLMs, frontier models, AI agents, RAG solutions, vector databases, APIs, and enterprise AI integrations. Identify and mitigate AI risks, including prompt injection, data leakage, model misuse, insecure tool use, and unauthorized access Conduct AI threat modeling, AI red teaming, adversarial testing, and validation of AI security controls. Use AI-enabled tools to discover vulnerabilities in applications, APIs, cloud environments, infrastructure, and enterprise systems. Apply AI to improve secure code review, vulnerability triage, configuration analysis, detection logic, and remediation guidance. Design and review secure AI architectures across AWS and Microsoft Azure. Assess cloud AI services such as Amazon Bedrock, Amazon SageMaker, Azure OpenAI Service, Azure Machine Learning, Azure AI Search, and Microsoft Copilot Review security architecture for applications, APIs, identity flows, cloud platforms, CI/CD pipelines, containers, serverless functions, and infrastructure as code. Develop scripts and automation using languages such as Python, PowerShell, Bash, or JavaScript. Support AI security standards, secure AI development guidelines, AI risk assessments, and AI incident response processes. Required Qualifications 5+ years of experience in cybersecurity, security architecture, application security, cloud security, DevSecOps, or security engineering. Strong understanding of threat modeling, vulnerability management, IAM, application security, API security, cloud security, data protection, logging, monitoring, and incident response. Experience securing AI/ML workloads, LLM applications, RAG pipelines, AI agents, or model-serving platforms. Hands-on experience with AWS and/or Microsoft Azure security controls. Experience with scripting or automation using Python or similar languages. Familiarity with AI security risks, including prompt injection, jailbreaks, data leakage, model misuse, insecure tool use, excessive agency, and adversarial attacks. Experience identifying vulnerabilities, misconfigurations, exposed secrets, insecure code patterns, and potential attack paths. Ability to translate technical risks into clear recommendations for engineering, architecture, risk, and leadership teams.
Preferred Qualifications
Experience using AI-assisted tools for vulnerability discovery, secure code review, threat modeling, log analysis, or security automation. Familiarity with OWASP Top 10 for LLM Applications, MITRE ATLAS, NIST AI RMF, secure MLOps, and LLMOps. Experience with SIEM, EDR, CSPM, CNAPP, SAST, DAST, SCA, vulnerability scanners, secrets management, Terraform, containers, or Kubernetes.