Senior Cloud Platform Engineer

Platform: Agentic AI Platform | Financial Services

Experience: 9+ Years Overall | 7+ Years Hands-on Cloud Infrastructure Engineering

We are looking for a deeply hands-on Senior Cloud Platform Engineer with strong multi-cloud, hybrid infrastructure, Kubernetes, networking, identity, and Infrastructure-as-Code experience to support an enterprise Agentic AI Platform in the financial services industry.

• Build and operate production Kubernetes runtimes for AI agents
• Design and implement cross-cloud networking, VPC/VNet peering, private endpoints, private DNS, NAT, and egress controls
• Establish runtime patterns across multiple hyperscalers
• Deliver private connectivity between cloud and on-premises infrastructure, including GPU environments
• Build infrastructure for AI workloads, including container registries, secrets, key management, service-to-service identity, inference endpoints, capacity, and quotas
• Establish secure connectivity for third-party and self-hosted services
• Own Terraform-based Infrastructure-as-Code, including modules, state management, and multi-environment promotion
• Collaborate with security, network, and infrastructure teams on enterprise changes and compliance requirements
• Create topology documentation, architecture decision records, and operational runbooks

• 7+ years of hands-on cloud infrastructure engineering
• Production experience with at least two major hyperscalers: Azure, AWS, or Google Cloud
• 5+ years of production Kubernetes experience, including self-operated/unmanaged clusters
• 4+ years of enterprise cloud networking
• 3+ years of hybrid cloud and on-premises connectivity
• 3+ years integrating third-party/self-hosted services into enterprise networks
• 4+ years of Terraform or equivalent Infrastructure-as-Code
• 3+ years building infrastructure for AI or data-intensive workloads
• Working knowledge of Azure AI Foundry, Azure OpenAI, AWS Bedrock, Google Vertex AI, or equivalent
• 2+ years building CI/CD pipelines using Git Hub Actions, Azure Dev Ops, or equivalent
• Working knowledge of LLM platform operations, including model endpoints, token throughput, quotas, rate limits, and inference cost

• CKA or comparable Kubernetes expertise
• Self-hosted or GPU-based inference infrastructure experience
• Financial services or regulated-industry experience
• Service mesh, Private Link, or Zero Trust networking experience
• Open Telemetry or enterprise observability experience
• AI gateway or model-routing experience