AI Security Engineer

Jobtailor; Denver (CO); On-site; USD 120,000 - 160,000; Full time

Job summary

Jobtailor is seeking an experienced security engineering/architecture professional to establish and operationalize security controls for AI/ML initiatives across the enterprise.

You will embed security into AI solution design, protect models and data pipelines, and enable secure adoption of AI use cases in collaboration with Digital, Data, AI, Security Architecture, Engineering, and Cyber Defense Operations teams.

Qualifications

• 5–8 years of cybersecurity engineering or security architecture experience, with exposure to cloud security, data protection, or application security.
• Experience working with enterprise AI, machine learning, analytics platforms, or data-driven technology environments.
• Practical understanding of AI/ML deployment patterns, APIs, model lifecycle, and enterprise data integration.
• Experience with Microsoft Azure AI services, OpenAI integrations, Databricks, or enterprise AI platforms preferred.
• Familiarity with emerging AI governance frameworks and responsible AI standards.
• Experience with Secure AI controls embedded into enterprise AI initiatives without slowing adoption.
• Security certifications such as CISSP, CCSP, or cloud security certifications preferred.

Responsibilities

• Establish and operationalize security controls for emerging AI capabilities across the enterprise.
• Embed security into AI solution design, protecting AI models and data pipelines, enabling secure adoption of AI use cases.
• Collaborate with Digital, Data, AI, Security Architecture, Engineering, and Cyber Defense Operations teams to define secure AI architecture patterns and guardrails.
• Define secure architecture patterns for AI/ML solutions, protecting models, training pipelines, inference environments, and data flows.
• Establish secure integration patterns for AI services across enterprise apps, APIs, cloud platforms, and data environments.
• Review AI solution designs to ensure alignment with security architecture standards and secure-by-design principles.
• Identify, assess, and mitigate AI-specific threats including model poisoning and data leakage.
• Define and implement security guardrails for AI model access, API usage, and prompts.
• Establish controls to protect training data, embeddings, prompts, and inference outputs.
• Support monitoring use cases for AI misuse and suspicious data movement.

Skills

Cybersecurity engineering

Security architecture

Azure AI services

OpenAI integrations

Databricks

AI governance

Secure AI controls

Security certifications

Tools

Azure

OpenAI platform

Databricks

Job description

Responsibilities

• Establish and operationalize security controls for emerging Artificial Intelligence and Machine Learning capabilities across the enterprise.
• Embed security into AI solution design, protecting AI models and data pipelines, and enabling secure adoption of AI use cases across business and technology functions.
• Work closely with Digital, Data, AI, Security Architecture, Engineering, and Cyber Defense Operations teams to define secure AI architecture patterns, implement guardrails, and ensure AI platforms operate within client’s cybersecurity, risk, and governance standards.
• Define secure architecture patterns for AI and machine learning solutions, ensuring protection of models, training pipelines, inference environments, and supporting data flows.
• Establish secure integration patterns for AI services across enterprise applications, APIs, cloud platforms, and data environments.
• Review AI solution designs to ensure alignment with enterprise security architecture standards and secure-by-design principles.
• Identify, assess, and mitigate AI-specific threats including model poisoning, prompt injection, adversarial attacks, unauthorized model access, data leakage, and misuse of AI outputs.
• Define and implement security guardrails for AI model access, API usage, prompt controls, and secure interaction with enterprise data sources.
• Establish controls to protect sensitive training data, embeddings, prompts, and inference outputs across AI workflows.
• Support development of monitoring use cases for AI misuse, abnormal model behavior, unauthorized access, and suspicious data movement.

Requirements

• 5–8 years of cybersecurity engineering or security architecture experience, with exposure to cloud security, data protection, or application security.
• Experience working with enterprise AI, machine learning, analytics platforms, or data-driven technology environments.
• Practical understanding of AI/ML deployment patterns, APIs, model lifecycle, and enterprise data integration.
• Experience with Microsoft Azure AI services, OpenAI integrations, Databricks, or enterprise AI platforms preferred.
• Familiarity with emerging AI governance frameworks and responsible AI standards.
• Experience with Secure AI controls embedded into enterprise AI initiatives without slowing adoption.
• Security certifications such as CISSP, CCSP, or cloud security certifications preferred.