Enterprise & Scientific AI Platform Security, Azure & GCP
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AI Security Engineer voor ASML AI Foundation, hybride Veldhoven, 40u/week, start 1 september, 6+ maanden.
- Locatie
- Veldhoven
- Werkvorm
- hybride
- Uren
- 40 uur per week
- Contractvorm
- zzp
- Startdatum
- Eerste waarneming
- Opdrachtgever
- ASML
Eisen
- Experience with Kubernetes (AKS/GKE), including GPU node pools
- A security-first mindset and the ability to translate control intent into defensible implementation
- Experience with IAM, RBAC/ABAC least-privilege design and managed/workload identity
- Strong understanding of network segmentation, VNet/VPC design and private endpoints
- Experience with policy-as-code and continuous compliance attestation
- Expert-level Terraform and infrastructure-as-code experience
- Ability to work effectively alongside senior specialists in a high talent density environment
- Hands-on cloud platform engineering experience with an AI/ML focus
- 5+ years of experience in software development lifecycle (SDLC) fundamentals
- Experience with GitOps and CI/CD pipelines (Azure DevOps or GitHub Actions)
- Hands-on experience with Azure and GCP
Wensen
- Experience with observability stacks such as OpenTelemetry, Prometheus, Loki, Tempo and Grafana
- Experience with MCP gateway or similar tool-level access control architectures
- Knowledge of ISO 27001, EU AI Act and GDPR compliance requirements
- Experience with data/model classification and model-theft prevention techniques
- Experience with LLM guardrails, prompt/response injection prevention and content filtering
Omschrijving
ASML's AI Foundation is scaling Enterprise AI and Scientific AI workloads across Azure and GCP, and every one of these workloads must be provably secure before it reaches production. As AI Security Engineer you translate the ASML AI Security Framework, control by control, into working platform configuration and the machine-verifiable evidence that proves adherence to it. This is not a policy or advisory function: you build the Terraform modules, GitOps pipelines and policy-as-code gates that make compliance continuous rather than a point-in-time audit exercise. You operate across Kubernetes GPU node pools, network segmentation, identity and the hardened API layer that sits between models and their consumers, and you instrument the platform end to end so anomalies and drift surface automatically. You work daily alongside Senior AI Security auditors and a high-density team of AI Engineers, in an environment where implementations are expected to pass governance the first time. It is a role with real technical depth and direct influence on how ASML secures one of its most strategically important platforms. Responsibilities include translating ASML AI Security Framework control descriptions into working platform configuration across Azure and GCP, engineering machine-verifiable evidence pipelines, building and maintaining Terraform modules, implementing GitOps CI/CD security gates in Azure DevOps and GitHub Actions, configuring and hardening AKS/GKE GPU node pools, designing VNet/VPC segmentation and private endpoints, implementing RBAC/ABAC least-privilege models, setting up geo-aware and risk-based access blocking controls, building a hardened API integration layer/proxy, configuring a centralized MCP gateway, implementing LLM guardrails, building observability with OpenTelemetry, Prometheus, Loki, Tempo and Grafana, enforcing data and model classification, and collaborating with Senior AI Security auditors to ensure ISO 27001, EU AI Act and GDPR governance. Must-haves include 5+ years of SDLC experience, hands-on cloud platform engineering with AI/ML focus, Azure and GCP experience, Kubernetes (AKS/GKE) including GPU node pools, expert-level Terraform, GitOps and CI/CD pipelines, IAM/RBAC/ABAC design, policy-as-code, network segmentation, and ability to work in high talent density environment. Nice-to-haves include LLM guardrails, observability stacks, ISO 27001/EU AI Act/GDPR knowledge, MCP gateway experience, and data/model classification. Competencies include strong stakeholder management, clear communication, analytical thinking, Agile/DevOps way of working, ownership, proactive attitude, detail-oriented mindset, collaboration, pragmatic problem solving, and continuous learning. Tech stack: Azure, GCP, Kubernetes (AKS, GKE), Terraform, Azure DevOps, GitHub Actions, OpenTelemetry, Prometheus, Loki, Tempo, Grafana, MCP gateway.
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