Cloud Platform Engineer

Responsibilities

• Design and operate secure, multi-account/tenant cloud landing zones for AI workloads (Azure primary, AWS secondary), including network segmentation, private connectivity, and egress controls.
• Provision and manage GPU-optimized compute and storage for training and inference (AKS/EKS GPU node pools, VM scale sets, blob/S3 object storage).
• Implement Infrastructure as Code with Terraform for all environments; enforce policy-as-code and guardrails.
• Establish identity, secrets, and access controls (Entra ID/AWS IAM, RBAC, Key Vault/KMS, HSM/PKI).
• Build observability and SRE practices (metrics, logs, tracing, alerting, runbooks, incident response, SLIs/SLOs).
• Harden environments for compliance (e.g., FedRAMP/FISMA-style controls), including vulnerability management and continuous compliance monitoring.
• Enable CI/CD for infrastructure and configuration (GitOps, automated drift detection, change management).
• Partner with AI Kubernetes Engineers to support reliable LLM deployment strategies (blue/green, canary, rollout/rollback) and capacity planning.
• Optimize cloud spend for GPU and storage workloads; forecast capacity and implement reservations/savings plans.

Qualifications

• At least 8 years relevant professional experience.
• Strong hands-on cloud engineering experience in one or more of Azure, AWS, GCP, including networking, identity, security, and automation.
• Expert Terraform skills; proven experience managing production infrastructure via IaC.
• Experience operating production Kubernetes clusters (AKS/EKS) supporting AI/LLM workloads.
• Proficiency with containers (Docker) and CI/CD.
• Solid scripting/programming abilities in Python or similar.
• Experience implementing observability (metrics, logging, tracing) and on-call operations.