AI Security Engineer

Role Overview

• 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.
• Support implementation of secure controls across AI development, testing, deployment, and production environments.
• 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 validation of third-party AI services and external model integrations from a cybersecurity risk perspective.
• Establish AI security standards, engineering guardrails, and governance practices aligned with regulatory requirements, enterprise risk expectations, and responsible AI principles.
• Partner with Digital and AI teams to enable secure AI use cases where security accelerates responsible business adoption rather than acts as a blocker.
• Support creation of AI security review checkpoints for new AI initiatives, pilots, and production deployments.
• Contribute to enterprise AI security policies, reference architectures, and operational standards.
• Collaborate with Cyber Defense Operations to operationalize AI-related detection, monitoring, and response capabilities.
• Support development of monitoring use cases for AI misuse, abnormal model behavior, unauthorized access, and suspicious data movement.
• Define logging and telemetry requirements for AI platforms to improve visibility and incident readiness.
• Support integration of AI platform telemetry into enterprise detection and monitoring tools where applicable.
• Work closely with Security Architecture, Cloud Engineering, Data teams, Application teams, and AI program owners to ensure consistent security adoption.
• Support security reviews for AI vendors, AI-enabled SaaS platforms, and internally developed AI capabilities.
• Provide technical guidance to project teams on secure AI implementation and operational controls.

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.
• Clear visibility into AI-related cyber risks and mitigation actions and ability to translate emerging AI risks into practical engineering controls.
• Practical AI guardrails established for data, model access, and operational use.
• Strong alignment between AI innovation, enterprise security, and regulatory expectations.
• Security certifications such as CISSP, CCSP, or cloud security certifications preferred.

Tech Stack

• Azure
• Cloud
• Cyber Security

Benefits

• Competitive base
• Comprehensive benefits and wellness support
• Flexible work model: hybrid, remote, or in-office
• Real growth opportunities and leadership visibility
• Inclusive, respectful culture that blends U.S. innovation with Colombian heart
• A company that listens, invests in you, and celebrates wins together