Security Engineer

🔥 1 minute ago

🇪🇺 Europe – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

👮‍♂️ Cybersecurity / Security Engineer

👻 Ghost score 12%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Cast AI

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2019

☁️ SaaS

🤖 Artificial Intelligence

🤝 B2B

SaaS • Artificial Intelligence • B2B

Cast AI is a SaaS platform that automates Kubernetes application performance, infrastructure, and cost optimization for cloud-native teams. It connects to EKS/AKS/GKE and on-prem clusters with no code changes, uses predictive models and runtime signals to rightsize pods, scale nodes, optimize GPUs and Spot instances, and run approved self-healing runbooks to maintain reliability and reduce cloud spend. Cast AI targets platform, SRE, and FinOps teams and supports AI/LLM workloads, GPU optimization, and real-time workload placement and cost intelligence.

📋 Description

• 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 • 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 • Present important risks to engineering and company leadership • Advise engineering teams on practical security improvements • Drive implementation of security controls across engineering, including production access, identity, secrets management and secure CI/CD practices • Ensure security controls are implemented, tracked and maintained • 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 security signals and incident-response practices while reducing alert noise • Use scripting, prototypes or code to validate risks, automate security workflows or support engineering teams • Report to Mantas Norvaiša and collaborate with engineering teams, the Platform team and compliance leadership

🎯 Requirements

• 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 • Strong understanding of Kubernetes and cloud-native infrastructure • Experience threat modelling real systems using STRIDE or a similar method • Strong software-engineering fundamentals, including software architecture, distributed systems and infrastructure • Ability to write or review code and automation in Go or Python • Strong written and spoken English • Proactive, adaptable approach and comfort working in a fast-moving environment • Nice to have: Kubernetes security, production cluster hardening, identity and secrets management, detection engineering, incident response, SOC 2, ISO 27001, FedRAMP, software supply-chain security, AI and agent-based systems security, Terraform, and security certifications such as OSCP or CISSP

🏖️ Benefits

• A flexible, remote-first working environment across Europe • Equity options • Learning and development budget, including access to relevant courses and conferences • Equipment budget and team-building support • Additional time off and company events • Direct influence over how security is designed, prioritised and implemented across the organisation • Exposure to large-scale multi-cloud, Kubernetes and AI infrastructure • Close collaboration with engineering and platform teams

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