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

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

Location:         Oaks, PA

Key Skills:       Cloud infrastructure, Azure, AWS, CI/CD, Cloud networking, Kubernetes, AI, Azure OpenAI

Mode of Hire:  Full Time

Skills Required:

•         7+ years hands-on experience in 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 experience in 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 experience in enterprise cloud networking: virtual networks and VPCs, subnets, peering, security groups, private endpoints, private DNS resolution and cross-network routing.

•         3+ years experience in 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 experience in 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 experience in infrastructure-as-code to production standard using Terraform or equivalent, including module design, state management and multi-environment promotion.

•         3+ years experience in 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 experience in 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.