Cloud Platform Engineer

Intapp • Charlotte, NC

1d

About The Position

The Cloud Platform Engineer will operate and evolve a Kubernetes-based platform for AI workloads, including LLM gateways, agent and tool-integration gateways, and agent runtimes. The role includes integrating and managing access to cloud-hosted foundation models across multiple providers, with attention to routing, quotas, fallbacks, cost controls, and tenant isolation. The engineer will deliver infrastructure as code and GitOps workflows; build monitoring, alerting, and tracing for AI workloads; and help design and prototype self-hosted model serving, including GPU workloads and inference runtimes.

The position will also harden the platform through secrets management, network policies, authentication and authorization, and audit logging. It will support internal product teams with documentation, onboarding, and troubleshooting. At Intapp, employees are offered professional development and continuing-education reimbursement, modern open offices, complimentary lunches, stocked kitchens, wellness programs, flexible time off, family-formation benefits, paid volunteer time, and a donation-matching program. Compensation may include variable compensation and equity in addition to base salary; base salary depends on factors including experience, location, and internal equity.

Requirements

• At least 3 years of experience in platform, DevOps, SRE, or cloud engineering.
• Hands-on Kubernetes experience, including deployments, networking, and troubleshooting.
• Experience with infrastructure as code (Terraform) and GitOps tooling such as Argo CD, Flux, or similar.
• Solid experience with at least one major cloud provider, 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 Haves

• 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.

Responsibilities

• 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, including 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.

Benefits

• Professional development opportunities.
• Reimbursement for training and continuing education.
• Modern, open offices.
• Complimentary lunches and fully stocked kitchens.
• Wellness programs.
• Flexible time off.
• Benefits designed to care for employees and their families.
• Family-formation benefits and support.
• Paid volunteer time off.
• Donation-matching program.
• Variable compensation and equity may be included in addition to base salary.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed