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# Cloud Platform Engineer

- **Company:** Intapp, Inc.
- **Location:** United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Audit Trail, User Authentication, Microsoft Azure, Bash Shell, Cloud Engineering, Computer Networks, Continuous Integration, DevOps, Python (Programming Language), Key Management, Octopus Deploy, Prometheus, Scripting, Cloud Platform System, Large Language Models, Grafana, Agentic-AI, Kubernetes, Software Coding, Model Context Protocol, Model Inference, Terraform
- **Published:** October 23, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=f7c76856b87566f3

## About the Role

* 3+ years in platform, DevOps, SRE, or cloud engineering
 * Hands-on experience with Kubernetes, including deployments, networking, and troubleshooting
 * Experience with infrastructure as code (Terraform) and GitOps tooling (Argo CD, Flux, or similar)
 * Solid experience with at least one major cloud, Azure or AWS
 * Experience with observability tools such as OpenTelemetry, Prometheus, and Grafana
 * Scripting and coding skills in Python, Go, or Bash, and CI/CD experience

Nice to have

 * Experience with LLM gateways or proxies, or LLM observability and tracing tools
 * Familiarity with managed model services from major cloud providers, such as Azure or AWS
 * Understanding of agent frameworks and tool-integration protocols such as Model Context Protocol (MCP)
 * Experience running GPU workloads or model inference serving
 * Experience in multi-tenant, regulated, or compliance-driven environments

## Description

* Operate and evolve a Kubernetes-based platform for AI workloads, including LLM gateways, agent and tool-integration gateways, and agent runtimes
 * Integrate and manage access to cloud-hosted foundation models across multiple providers, covering routing, quotas, fallbacks, cost controls, and tenant isolation
 * Deliver infrastructure as code and GitOps workflows
 * Build monitoring, alerting, and tracing for AI workloads
 * Help design and prototype self-hosted model serving, including GPU workloads and inference runtimes
 * Harden the platform with secrets management, network policies, authentication and authorization, and audit logging
 * Support internal product teams through documentation, onboarding, and troubleshooting