Senior Cloud Platform Engineer (Multi-Cloud & Hybrid Infrastructure)

Location :Oaks,PA, Hybrid 3 days onsite

MUST HAVE

• 7+ years hands-on cloud infrastructure engineering in production environments, covering compute, networking, identity, storage and monitoring.
• Production depth on at least two major hyperscalers (Azure, AWS or Google Cloud), including the networking and identity model of each. Breadth here is a defining requirement, not a bonus: you will be expected to design across cloud boundaries rather than within one.
• 5+ years Kubernetes in production, including self-operated or unmanaged clusters. Cluster networking, ingress, workload identity, autoscaling and cluster security, not managed control planes alone.
• 4+ years enterprise cloud networking: virtual networks and VPCs, subnets, peering, security groups, private endpoints, private DNS resolution and cross-network routing.
• 3+ years hybrid cloud and on-premises connectivity, using ExpressRoute, Direct Connect, site-to-site VPN or equivalent, including DNS resolution across boundaries and firewall and egress rules. On-premises infrastructure is a first-class runtime target on this platform, not an edge case.
• 3+ years integrating third-party or self-hosted services into an enterprise network, including private connectivity or controlled egress, DNS resolution, TLS and certificate management, authentication between systems, and working the firewall and security review needed to get each path approved.
• 4+ years infrastructure-as-code to production standard using Terraform or equivalent, including module design, state management and multi-environment promotion.
• 3+ years building infrastructure for AI or data-intensive workloads, on any major cloud: inference or model endpoints, container registries and image supply chain, service-to-service identity, secrets management, and the capacity and quota model these workloads run under.
• Working knowledge of the AI service landscape across hyperscalers: Azure AI Foundry and Azure OpenAI, AWS Bedrock, Google Vertex AI or equivalent, and an understanding of what differs between them in networking, identity and cost.
• 2+ years building CI/CD pipelines for infrastructure delivery using GitHub Actions, Azure DevOps or equivalent.
• Working knowledge of LLM platform operations: model endpoints, token throughput, quotas and rate limits, and how inference cost accrues and is attributed.