Cloud Platform Engineer - Google Cloud (GCP)

Arden University

1d•Remote

About The Position

The Cloud Platform Engineer will design, build and operate Arden University’s Google Cloud platform, including the infrastructure and services underpinning production Agentic AI capabilities. The role establishes secure, resilient and scalable cloud foundations, develops reusable platform capabilities and strengthens DevOps practices so product, integration, data and AI teams can deliver services safely and efficiently. The engineer combines hands-on cloud engineering with automation, infrastructure as code and platform operations to keep environments consistent, well-governed, observable and cost-effective. Working with teams across Platform, Infrastructure, Cyber Security, Development, Integration, Data and AI, the role embeds security, reliability and operational best practice throughout the cloud lifecycle. It also enables evolving Agentic AI capabilities through secure access to models and tools, managed identities, retrieval services, runtime controls and monitoring, supporting the university’s digital transformation and multi-cloud strategy. The role is permanent, 37 hours per week, and operates primarily remotely, with little to no travel to the Coventry head office or campuses. An Arden work location is included in the employment contract. Skilled Worker visa sponsorship is not available for this role.

Requirements

Substantial hands-on experience engineering and operating production workloads on Google Cloud, including building and maintaining landing zones and cloud foundations.
Strong knowledge of Google Cloud resource hierarchy, organisation policies, IAM, service accounts, Workload Identity, networking and secure project configuration.
Practical experience with Google Cloud services including GKE, Cloud Run, Compute Engine, Cloud Storage, Cloud SQL, Pub/Sub, Cloud DNS, load balancing, Cloud Logging and Cloud Monitoring.
Experience deploying and scaling Kubernetes and containerised services across public or private cloud environments, including GKE, EKS or AKS, alongside serverless workloads.
Proficiency in Terraform, reusable modules and Infrastructure as Code.
Experience implementing CI/CD pipelines using tools such as Cloud Build, GitHub Actions, GitLab CI or Azure DevOps.
Strong scripting and automation skills using Bash, Python, the Google Cloud CLI and Git, with practical knowledge of Linux, networking, APIs and SQL.
Experience implementing cloud security controls, including encryption, secrets management, network security, vulnerability remediation, data protection and auditable guardrails.
Experience supporting production generative AI and Agentic AI services on Google Cloud, including model and agent runtimes, RAG and data grounding, tool integration, agent identities, secure access, observability and evaluation.
Strong understanding of cloud monitoring and operational reliability.
Experience managing cloud consumption, budgets, forecasts, capacity and cost optimisation, including showback and chargeback practices.
Understanding of cloud reliability, performance, scalability and capacity planning, balancing cost efficiency with operational resilience.

Nice To Haves

Certifications in AWS, Microsoft Azure or Kubernetes.
Experience with AWS and/or Microsoft Azure in hybrid or multi-cloud environments.
Familiarity with additional Google Cloud services such as Apigee, Eventarc, BigQuery, Cloud SQL, Spanner, Secret Manager, Cloud KMS, Security Command Center and Vertex AI.
Experience supporting data, integration, machine learning, generative AI or Agentic AI workloads, including managed agent runtimes, RAG, vector search and AI security controls.
Experience with cloud migration, platform engineering, developer self-service, policy-as-code or configuration compliance.
Understanding of software architecture, API and event-driven integration, and site reliability engineering practices.
Experience working within security and risk-management frameworks such as ISO 27001 or Cyber Essentials.
Experience working in Agile or Kanban teams and following formal incident, problem, change and release-management processes.

Responsibilities

Design, build and operate the university’s Google Cloud platform and production cloud infrastructure.
Build and maintain secure, resilient and scalable cloud foundations and landing zones.
Develop reusable platform capabilities and strengthen DevOps practices.
Use automation, infrastructure as code and platform operations to ensure environments are consistent, governed, observable and cost-effective.
Embed security, reliability and operational best practices throughout the cloud lifecycle.
Work with Platform, Infrastructure, Cyber Security, Development, Integration, Data and AI teams.
Enable Agentic AI capabilities through secure model and tool access, managed identities, retrieval services, runtime controls and monitoring.
Manage cloud consumption, budgets, forecasts, capacity and cost optimisation, including showback and chargeback practices.
Support cloud reliability, performance, scalability and capacity planning while balancing cost efficiency and operational resilience.