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# AI Security Engineer

- **Company:** ProLogis
- **Location:** Denver, CO, United States
- **Experience:** Experienced
- **Salary:** $106,000.0 - $146,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Software as a Service, Cloud Computing Security, Cyber Security, Data Governance, Information Leak Prevention, Programming Tools, Identity and Access Management, Machine Learning, Open Web Application Security, OpenAI, Microsoft Copilot, Secure Coding, Cloud Platform System, GitHub Copilot, Large Language Models, Prompt Engineering, Software Security, Llamaindex, AI Platforms, Machine Learning Operations, GPT, Devsecops, Static Application Security Testing, Dynamic Application Security Testing
- **Published:** October 31, 2026
- **Apply:** https://www.juju.com/job/1_58631FAC68574E3D842122174C0FE215-CO844768663516164

## About the Role

application teams to ensure secure deployment of AI capabilities - Evaluate and onboard AI vendors and tools, ensuring alignment with security, privacy, and compliance requirements - Promote awareness and adoption of secure AI usage practices across the organization - Remain current on emerging AI and security risks, trends, and technologies - Demonstrate willingness and capability to leverage emerging technology, automation, and AI tools to improve efficiency, quality, and speed.
Exercises sound judgment, creative thinking, and accountability for outcomes. **Building blocks for success** **Required:** - Strong hands-on experience with AI/ML technologies, LLMs, or AI development tools - 3-5+ years of experience in security engineering, application security, or cloud security - Experience performing threat modeling, security architecture design, and secure code review or testing - Experience developing AI solutions within IDEs, utilizing AI code assistants - Experience working with LLM APIs (OpenAI, Anthropic, etc.) - Familiarity with AI frameworks such as LangChain, LlamaIndex, or similar - Understanding of AI/ML lifecycle and prompt engineering - Familiarity with AI security risks such as prompt injection, data leakage, and model misuse - Experience working in cloud environments (AWS, Azure, or GCP) - Familiarity with secure development practices (DevSecOps) - Working knowledge of OWASP Top 10 and application security
principles - Strong collaboration and communication skills **Preferred:** - Experience with API security, identity and access management, and data protection controls - E perience with SAST, DAST, or code scanning tools - Familiarity with privacy, compliance, and data governance concepts - Experience evaluating or securing SaaS platforms and APIs - Background in DevSecOps or security engineering Hiring Salary Range of: $106,000 - $146,000.

## Description

You should be willing to learn whatever it takes to get the job done-from experimenting with new AI tools, to integrating AI into workflows, to ensuring safe and responsible use of AI technologies.
You can expect to work across engineering, security, and business teams, as well as with vendors, to evaluate and implement AI solutions.
Primary goals are to enable AI adoption, protect company data, and establish best practices for secure and effective AI usage. **Key responsibilities include:** - Design, build, and support AI/ML solutions and integrations across the enterprise - Evaluate and secure AI platforms, LLMs, copilots, and AI-powered development tools (e.g., GitHub Copilot, ChatGPT, etc.) - Lead development of AI security controls, guardrails, and governance frameworks - Perform threat modeling and risk assessments for AI/ML systems and integrations - Partner with engineering teams to enable secure AI development practices, including prompt engineering, API security, and data protection - Assess and mitigate risks related to LLMs, including prompt injection, model leakage, and data exposure - Contribute to secure architecture patterns for AI-enabled applications and services - Support security reviews, testing, and validation of AI use cases and implementations - Collaborate with cloud, data, and