AI Security Engineer

Shrive Technologies LLC; Town of Texas (WI); On-site; USD 120,000 - 180,000; Full time

Job summary

Shrive Technologies LLC in the United States is seeking an AI Engineer to build and operationalize tooling, pipelines, and controls that secure our growing AI initiatives across the enterprise.

This hands-on role implements AI/ML security controls, integrates AI risk signals into existing security tooling, and collaborates with data science and platform teams to embed security into the AI development lifecycle.

Qualifications

• 3+ years in security engineering or ML engineering with hands-on AI/LLM exposure.
• Proficiency in Python and ML/LLM frameworks (e.g., LangChain, Hugging Face, TensorFlow/PyTorch) or AI security tooling.
• Cloud platforms (Azure/AWS/GCP) knowledge and cloud-native security controls.
• Familiarity with AI threat models: prompt injection, model inversion, data poisoning, insecure output handling.
• Experience with CI/CD, infrastructure-as-code, and integrating security tooling into automated pipelines.

Responsibilities

• Design, build, and maintain tooling for AI/ML asset discovery, model inventory, and shadow-AI detection across the enterprise.
• Implement security controls for AI pipelines, including data protection, access control, secrets management, and secure model deployment (MLOps/LLMOps security).
• Integrate AI risk signals into SIEM/SOAR and monitoring platforms.
• Build automated testing and red-teaming harnesses for AI applications (adversarial testing, jailbreak/prompt-injection testing, data leakage testing).
• Support secure integration of third-party and internally built AI/LLM services (API gateways, guardrail middleware, output filtering).
• Collaborate with data science, platform engineering, and application teams to embed security requirements into the AI development lifecycle (secure-by-design, CI/CD gates).
• Document control implementations, runbooks, and technical standards for AI security engineering.

Skills

Python

ML/LLM frameworks

Cloud platforms

CI/CD pipelines

Security tooling

AI security concepts

Education

Bachelor's degree in CS or related field

Security/cloud certifications

Tools

Guardrail frameworks

Model scanning tools

Splunk

Job description

The AI Engineer will build and operationalize the tooling, pipelines, and controls that secure Ulta's growing portfolio of AI initiatives.

This is a hands-on engineering role responsible for implementing AI/ML security controls, integrating AI risk detection into existing security tooling, and supporting secure-by-design AI development practices across the enterprise

Key Responsibilities

• Design, build, and maintain tooling for AI/ML asset discovery, model inventory, and shadow-AI detection across the enterprise.
• Implement security controls for AI pipelines, including data protection, access control, secrets management, and secure model deployment (MLOps/LLMOps security).
• Integrate AI risk signals (e.g., prompt injection attempts, data exfiltration via AI tools, anomalous model behavior) into existing SIEM/SOAR and monitoring platforms.
• Build automated testing and red-teaming harnesses for AI applications (adversarial testing, jailbreak/prompt-injection testing, data leakage testing).
• Support secure integration of third-party and internally built AI/LLM services (API gateways, guardrail middleware, output filtering).
• Collaborate with data science, platform engineering, and application teams to embed security requirements into the AI development lifecycle (secure-by-design, CI/CD gates).
• Document control implementations, runbooks, and technical standards for AI security engineering.

Skill Requirements

Required Qualifications

• 3+ years in security engineering, cloud engineering, or ML engineering, with direct hands-on exposure to AI/ML or LLM-based systems.
• Proficiency in Python and experience with ML/LLM frameworks (e.g., LangChain, Hugging Face, TensorFlow/PyTorch) or AI security tooling (e.g., guardrail frameworks, model scanning tools).
• Working knowledge of cloud platforms (Azure and/or AWS/GCP) and cloud-native security controls (IAM, network segmentation, key/secrets management).
• Familiarity with AI-specific threat models: prompt injection, model inversion, data poisoning, insecure output handling, excessive agency (OWASP Top 10 for LLM Applications).
• Experience with CI/CD pipelines, infrastructure-as-code, and integrating security tooling into automated pipelines.

Preferred Qualifications

• Experience with SIEM/SOAR platforms (Splunk, Sentinel, etc.) and scripting integrations.
• Security certifications (Security+, GCIH, OSCP) or cloud certifications (AWS/Azure Security).
• Prior retail or PCI-regulated environment experience.