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# AI Platform Infrastructure Engineer

- **Company:** Luxoft
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Kubernetes Security, Artificial Intelligence, Amazon Web Services, Computer Networks, Continuous Integration, DevOps, Github, Identity and Access Management, Python (Programming Language), Octopus Deploy, Role-Based Access Control, AI Infrastructure, Pulumi, Cloud Platform System, Istio, Multi-Agent Systems, Amazon Virtual Private Cloud (VPC), Cloudformation, AI Platforms, Kubernetes, Infrastructure Automation Frameworks, Virtual Agents, Terraform, Jenkins
- **Published:** June 7, 2026
- **Apply:** https://uk.indeed.com/viewjob?jk=81432e27333d3229

## About the Role

Do you have experience in Terraform?, Do you have a Bachelor's degree?, Must have

Deep hands-on experience with Kubernetes (EKS preferred) - cluster design, networking (CNI, service mesh), RBAC, and security policies

Strong AWS expertise - VPC architecture, IAM, ECS/EKS, networking, and security groups

Proficiency with Infrastructure-as-Code tools such as Terraform, CloudFormation, or Pulumi

Experience designing container architectures for multi-tenant or security-sensitive workloads

Familiarity with GitOps workflows and CI/CD platforms (ArgoCD, GitHub Actions, Jenkins)

BS + 5 years or MS + 3 years in a platform/infrastructure/DevOps engineering role

Python and/or Go for tooling and automation

Up-to-date with the latest advancements in cloud-native infrastructure, container security, and AI workload deployment

Nice to have

Familiarity with AI infrastructure -agent orchestration frameworks, MCP

Experience in the financial domain - regulated environments, compliance-aware infrastructure

Experience with container orchestration platforms for autonomous or long-running AI workloads

Prior experience securing production AI agent workloads at scale

## Description

The engineer will design container architectures for security-sensitive and multi-tenant environments, implement network policies and access controls for workload isolation, build CI/CD pipelines for reliable deployment, and establish infrastructure-as-code practices for reproducibility and auditability. They will work closely with AI engineers, but their primary focus is the platform itself - making it secure, scalable, and operationally excellent.


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