AI-Driven Cloud Platform Engineer (Kubernetes, Go/Python)

Salesforce, Inc.; Palo Alto (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Salesforce, Inc. is seeking a Cloud Infrastructure Engineer to join our Platform Engineering team. You will manage Kubernetes clusters across AWS, Azure, GCP, and OCI, building scalable services in Go and Python and advancing our GitOps and IaC pipelines.

You will collaborate with SRE, security, and product engineers to deliver self-service capabilities, reduce toil, and apply AI-assisted engineering practices for faster, safer deployments.

Qualifications

• 2–4 years of professional experience in cloud infrastructure engineering and continuous deployment.
• Hands-on Kubernetes experience — deploying, troubleshooting, and automating workloads or clusters.
• Good programming skills in Golang and/or Python, with experience building services, CLIs, or automation tooling.

Responsibilities

• Build and maintain platform services and automation in Go and Python that help manage Kubernetes clusters across AWS, Azure, GCP, and OCI.
• Contribute to continuous deployment pipelines using GitOps tooling (Flux, Argo CD) and infrastructure-as-code frameworks (Pulumi, Terraform).
• Take part in fleet initiatives such as cluster lifecycle automation, upgrades, policy enforcement, and observability improvements.
• Join on-call rotations as you ramp and help improve the operational posture of the platform through automation and post-incident learning.

Skills

Kubernetes

Golang

Python

Cloud platforms (AWS)

GitOps (Flux/Argo CD)

Pulumi/Terraform

AI literacy

Communication

Tools

Flux

Argo CD

Pulumi

Terraform

Job description

Salesforce, Inc. is seeking a Cloud Infrastructure Engineer to join our Platform Engineering team. You will manage Kubernetes clusters across AWS, Azure, GCP, and OCI, building scalable services in Go and Python and advancing our GitOps and IaC pipelines.

You will collaborate with SRE, security, and product engineers to deliver self-service capabilities, reduce toil, and apply AI-assisted engineering practices for faster, safer deployments.