Platform Engineer - Remote AI Infra & Kubernetes

Vectara, Inc.; Palo Alto, Northern (CA, KY); Hybrid; USD 180,000 - 250,000; Full time

Benefits offered by this job

Medical, Dental, Vision coverage

PTO, sick time and holidays

Professional development opportunities

Job summary

Vectara, Inc. is seeking a hands-on Platform Engineer to own the infrastructure for our deploy-anywhere platform. You’ll manage Kubernetes clusters, CI/CD, IaC, and observability, across AWS, GCP, and on-prem environments including air-gapped setups.

You’ll contribute to backend services and scale ML/NLP workloads, while improving reliability and developer experience for a global enterprise customer base. Office in Palo Alto preferred.

Qualifications

• 2+ years in platform engineering, DevOps, SRE, or backend infra roles.
• Strong Kubernetes experience (deployment, debugging, scaling).
• Hands-on with infrastructure-as-code: Terraform, Helm, or Pulumi.
• Experience with at least one major cloud provider (AWS preferred).
• Proficiency in Go, Python, or Java and backend codebases.

Responsibilities

• Build and maintain IaC for AWS EKS, GCP GKE, and on-prem deployments.
• Own CI/CD pipelines and GitOps adoption.
• Deploy, scale, and optimize ML/NLP inference workloads on Kubernetes.
• Improve observability with Prometheus, Grafana, Datadog, and OpenTelemetry.
• Collaborate with Field Engineering for PoCs and deployments in customer clouds.
• Contribute to backend services and platform features; improve reliability and scalability.

Skills

Kubernetes

Terraform

Helm

Pulumi

Cloud (AWS/GCP/Azure)

Go

Python

Java

CI/CD

Linux

Networking

Distributed systems

Tools

Prometheus

Grafana

Datadog

OpenTelemetry

GitHub Actions

Bazel

ArgoCD

Job description

Vectara, Inc. is seeking a hands-on Platform Engineer to own the infrastructure for our deploy-anywhere platform. You’ll manage Kubernetes clusters, CI/CD, IaC, and observability, across AWS, GCP, and on-prem environments including air-gapped setups.

You’ll contribute to backend services and scale ML/NLP workloads, while improving reliability and developer experience for a global enterprise customer base. Office in Palo Alto preferred.