Security AI DevSecOps Engineer

Role overview

Pipeline and software security

• Design CI/CD controls for SAST, DAST, SCA, container image scanning, and secrets detection.
• Enforce policy as code and perform infrastructure-as-code security reviews across Terraform and CloudFormation environments.
• Lead SBOM generation, artifact signing, and dependency governance; implement code security standards and report compliance.
• Ensure API security aligns with best practices.

AI/ML security and development

• Threat-model LLM applications, model training pipelines, and inference-serving layers with AI/ML teams.
• Implement defenses aligned to OWASP LLM Top 10 and MITRE ATLAS for prompt injection, data exfiltration, model inversion, and adversarial inputs.
• Secure MLOps workflows, model registries, dataset pipelines, and deployment platforms; assess AI vendors, model integrations, and supply chain risks.
• Build internal AI agents for security operations using LLM APIs and orchestration frameworks; establish prompt governance, output validation, and audit logging.

Partnership, cloud, and reporting

• Provide secure-by-default patterns, reusable pipeline components, templates, and developer runbooks; facilitate threat-modeling workshops and security design reviews.
• Improve secrets management and cloud security posture, including network security and container/Kubernetes hardening; support incident response.
• Define and monitor pipeline-security and AI-agent metrics, and report program maturity, AI security posture, and automation impact to leadership.

Minimum qualifications

• Bachelor's degree in Computer Science, Information Security, Information Technology, or a related field.
• 4–7 years of security engineering experience, including AppSec, DevSecOps, cloud security, or a closely related area.
• Hands-on CI/CD pipeline security experience using modern tools such as Snyk, Semgrep, Checkmarx, Trivy, or Checkov.
• Strong Python or similar scripting proficiency; working knowledge of LLM security risks and experience building with LLM APIs or AI orchestration frameworks.
• Cloud security experience in AWS, Azure, or GCP covering IAM, network security, secrets management, and container/Kubernetes hardening.
• Ability to collaborate with engineering teams and communicate security concepts to non-security audiences.

Preferred qualifications

• A relevant master's degree is preferred.
• Experience with MLOps platforms, agentic AI architectures, or SIEM/SOAR platforms and security data pipelines is preferred.
• Preferred certifications: CISSP; OSCP or OSWE; AWS Certified Security – Specialty or equivalent; CAISP or comparable AI security credential; CDP or CDE.

Critical competencies

• Knowledge of DevSecOps, secure SDLC practices, CI/CD security tooling, AI/ML threats, OWASP LLM Top 10, and MITRE ATLAS.
• Ability to build and operate production security AI agents and automation workflows.
• Strong communication, project management, organization, collaboration, independent working, and influence without direct authority.

Working conditions

• Hybrid work is permitted. In-person work is available at offices in Greenville, South Carolina, and Plano, Texas.
• Some travel may be required, less than 10%.