Senior AI Platform Engineer - Security

About This Role

This engineering role focuses on building shared security foundations for Esko's AI Platform, enabling product teams to develop AI capabilities securely. The position involves taking end-to-end ownership of substantial security areas, collaborating with various teams to develop secure, practical, and reusable controls across products. It is a hands-on engineering role, not a compliance or review position.

Responsibilities

• Help shape the AI Platform's security architecture and take end-to-end ownership of substantial security components
• Design and build identity, authorization, delegated-access, and policy-enforcement controls for AI systems, including tenant-, user-, document-, and tool-level permissions
• Secure retrieval, tool use, and execution against prompt injection, unauthorized actions, data leakage, and other AI-specific threats
• Design controls proportionate to the risk of an action, including approval gates, isolation, execution limits, and recovery mechanisms
• Build audit and monitoring capabilities so security-relevant activity is traceable and high-risk behavior can be detected, investigated, and routed for appropriate review
• Conduct threat modelling and adversarial testing, translating findings into shared platform controls and secure defaults for product teams
• Work with cloud platform teams, security engineers, compliance specialists, and product engineering teams to integrate controls that satisfy Esko's security obligations while remaining practical to adopt and operate

Requirements

• Strong software engineering experience designing, building, and operating secure production systems in Python, TypeScript, or a similar language
• At least 5 years

experience in a software engineering role with experience securing cloud, distributed, or platform services, including authentication, authorization, policy enforcement, and multi-tenant isolation

• Practical experience with threat modelling, application security testing, and turning identified risks into effective engineering controls
• Experience designing or building systems using LLMs, retrieval-augmented generation, or agents, with an understanding of their distinctive security failure modes
• Experience building reusable platform controls, APIs, or shared services used by multiple engineering teams
• Sound judgement in balancing security, usability, and operational complexity, including recognizing when stronger isolation or human approval is required
• Ability to communicate security decisions and tradeoffs clearly across engineering, security, compliance, and product stakeholders
• Undergraduate, Master

degree or PhD in Computer Science / Machine Learning / Data Science / Artificial Intelligence, or related disciplines