AI & Multi-Cloud Architecture Lead (Hands-On)
Overview
Own and drive the multi-cloud architecture strategy for AWS and Google Cloud Platform, setting the technical direction for enterprise-scale AI and data workloads. This hands-on role combines architecture definition with direct build work, partnering with data engineering and infrastructure teams to deliver cloud-agnostic solutions. You will balance enterprise standards with practical delivery, owning the blueprint and coding critical components. This contract role in Juno Beach, Florida, offers direct influence on cloud adoption and AI enablement.
What You'll Do8
- 1Design multi-cloud reference architectures that run identically on AWS and Google Cloud Platform.
- 2Build infrastructure-as-code modules using Terraform or Google Deployment Manager to automate cloud resource provisioning.
- 3Lead migration of existing workloads from on-premises to AWS and GCP, defining the landing zone and network topology.
- 4Collaborate with data engineering to deploy scalable data pipelines using Apache Spark and Kafka on managed services like Amazon EMR and Google Dataproc.
- 5Define security and identity policies across both clouds, implementing IAM roles and organization policies.
- 6Drive the adoption of Kubernetes for containerized workloads, configuring Amazon EKS and Google Kubernetes Engine clusters.
- 7Evaluate and integrate AI/ML services such as Amazon SageMaker and Google Vertex AI, optimizing for cost and performance.
- 8Develop internal tooling to monitor and manage multi-cloud resources, writing Python or Go scripts to automate operations.
Requirements9
- 17+ years in cloud architecture, data engineering, infrastructure engineering, or platform engineering.
- 2Hands-on experience designing and implementing solutions on AWS and Google Cloud Platform.
- 3Deep understanding of multi-cloud and cloud-agnostic architecture principles.
- 4Proven ability to define enterprise architecture standards while working hands-on with engineering teams.
- 5Experience with infrastructure-as-code tools like Terraform or Google Deployment Manager.
- 6Strong knowledge of Kubernetes, including Amazon EKS and Google Kubernetes Engine.
- 7Familiarity with data engineering frameworks such as Apache Spark and Kafka.
- 8Working knowledge of Python or Go for scripting and tool development.
- 9Excellent problem-solving and stakeholder communication skills.
Salary Insight
Salary not disclosed in listing
Location
Required Skills
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