Security Engineer

CAST AI is an automation platform that operates cloud-native and AI infrastructure  embedding autonomous decision-making directly into Kubernetes and cloud environments, CAST AI continuously optimises performance, reliability and efficiency in production. More than 2,100 companies rely on CAST AI , including Akamai, BMW, Cisco, FICO, Hugging Face, NielsenIQ, Swisscom and TGS. We are headquartered in Miami and operate as a remote-first company across 34 countries in Europe, North America, Latin America and APAC.

In January 2026, CAST AI reached unicorn status following a strategic investment from Pacific Alliance Ventures, the corporate venture arm of Shinsegae Group. About the role CAST AI is hiring its first dedicated Security Engineer. Security is currently handled alongside other responsibilities by engineers across the product organisation. As the company grows, we need someone whose full-time focus is understanding the security of our platform, identifying the highest-impact risks and helping engineering teams address them.

CAST AI operates inside customers' production environments. Customers grant the platform cloud credentials, cluster access and permission to delete, move and resize workloads. Every permission needs to be justified, every action needs to be attributable and every component needs to be assessed as part of a wider attack surface. You will work across a multi-tenant platform spanning AWS, GCP and Azure, Kubernetes environments, microservices, CI/CD pipelines and AI agents that make autonomous decisions in production.

You will help make the secure path the easiest path for engineering teams. This is a technical, hands-on individual contributor role. You will not be expected to implement every security control yourself, but you will identify risks, define practical mitigations, guide the teams responsible for implementation and drive the most important issues through to closure. You will report to Mantas Norvaiša and work closely with engineering teams, the Platform team and Danielius, who leads the non-technical side of compliance.

• Learn CAST AI 's architecture, infrastructure and engineering workflows, then identify security flaws and material risks.
• Set up, operate and continuously tune cloud security tooling such as Wiz so it produces high-signal findings that engineering teams can act on.
• Build threat models for the product and backend architecture, including trust boundaries, service-to-service authentication, customer-cluster access, secrets and attack paths.
• Assess and prioritise findings using evidence, business impact and exploitability rather than treating every alert equally.
• Present the most important risks to engineering and company leadership and build the case for addressing them.
• Advise engineering teams on practical security improvements.
• Drive the implementation of security controls across engineering, including production access, identity, secrets management and secure CI/CD practices.
• Work with the relevant teams to ensure security controls are implemented, tracked and maintained.
• Help translate compliance requirements into meaningful technical work and evidence.
• Contribute to security standards for cloud infrastructure, Kubernetes, software supply chains and AI-enabled systems.
• Support the development of security signals and incident-response practices while helping reduce alert noise.
• Use scripting, prototypes or code where helpful to validate risks, automate security workflows or support engineering teams.

• At least 5-6 years of professional security experience.
• Around 10 years or more would be ideal.
• Hands-on experience setting up and running cloud security tooling in production, such as Wiz, Upwind or a similar platform, and turning findings into fixes.
• Strong understanding of cloud infrastructure security across compute, networking, IAM, storage and service communication.
• Practical experience with at least one of AWS, GCP or Azure, with the ability to reason about how cloud systems work and how they can fail.
• Strong understanding of Kubernetes and cloud-native infrastructure.
• Experience threat modelling real systems using STRIDE or a similar method, and the ability to lead engineering teams through the process.
• Strong software-engineering fundamentals, including the ability to understand and reason about software architecture, distributed systems and infrastructure.
• Ability to write or review code and automation in Go or Python.
• Strong written and spoken…