Platform Engineer at Vectara – United States | Jobyssey

You’ll own the infrastructure that runs our deploy anywhere platform — from Kubernetes clusters serving ML inference at scale to the CI/CD pipelines, IaC, and observability stack that keep it all reliable
This is a hands-on role: you’ll write Helm charts and Terraform one day, debug a Kafka consumer lag issue the next, and ship a backend service feature the day after
You’ll deploy across AWS, GCP, and on-premises (including air-gapped environments), and you’ll participate in an on-call rotation supporting enterprise customers
Build and maintain infrastructure-as-code (Terraform, Helm) for our AWS EKS and GCP GKE clusters, plus on-premises deployments (including Tanzu and air-gapped environments)
Own CI/CD pipelines (GitHub Actions, Bazel, ArgoCD) and drive GitOps adoption
Deploy, scale, and optimize ML/NLP inference workloads (vLLM, PyTorch, GPU scheduling with various Kubernetes scalers)
Build and improve observability: Prometheus, Grafana, Datadog,, and OpenTelemetry
Collaborate with Field Engineering to support PoCs and platform deployments in customer cloud VPCs and on-prem environments
Contribute to backend services (Java 21, Python, gRPC) and platform features
Improve system reliability, scalability, and developer experience across the engineering org

Solid fundamentals in Linux, networking, and distributed systems
Hands-on with infrastructure-as-code: Terraform, Helm, or Pulumi
Experience with at least one major cloud provider (AWS preferred; GCP or Azure also valued)
Working knowledge of CI/CD systems (GitHub Actions, Bazel, ArgoCD, or similar)
Proficiency in one or more of: Go, Python, Java. Comfortable reading and contributing to backend codebases
Strong Kubernetes experience (deployment, debugging, scaling — not just kubectl apply)
2+ years in platform engineering, DevOps, SRE, or backend infrastructure roles
Experience deploying or operating ML inference workloads (model serving, GPU scheduling, vLLM, TensorFlow Serving, or similar)
Familiarity with streaming/messaging systems (Kafka, Pulsar) and data stores (MariaDB/PostgreSQL, Aerospike, ClickHouse, OpenSearch)
Experience with GitOps workflows (ArgoCD, Flux)
Exposure to air-gapped or on-premises Kubernetes deployments
Background in observability tooling (Prometheus, Grafana, OpenTelemetry, Datadog)
Experience providing technical support or working directly with enterprise customers on infrastructure issues
Comfort with AI-assisted development workflows and managing AI coding agents

Platform Engineer

Similar jobs