Platform Engineer - Real-Time Infra for Voice AI

Precision Labs

San Francisco, Northern (CA, KY)

Hybrid

USD 140,000 - 210,000

Full time

9 days ago

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Benefits offered by this job

Competitive salary

Employee equity

SF office

Health/dental/vision insurance

Free lunch & dinner at the office

Job summary

Simple AI in San Francisco is seeking a Platform Engineer to build and run the infrastructure behind our real-time voice conversations. You will own AWS/Kubernetes infrastructure, implement Terraform modules, and ensure reliability and scalability without impacting customers.

You'll work closely with founders and an engineering team to ship robust platform components, improve observability, and optimize costs while delivering high-availability services for enterprise clients.

Qualifications

Hands-on experience running Kubernetes and AWS infrastructure in production.
Strong Terraform experience, including maintainable modules, state management, and safe infrastructure changes.
Solid understanding of Linux, containers, networking, and distributed systems.
Experience building CI/CD pipelines and operating services through deployments, incidents, and growth.
Ability to write software and automation in a language such as Python, Go, or TypeScript.
Good judgment about when to introduce new infrastructure and when to keep things simple.
Someone who takes ownership, communicates clearly, and follows problems through to a lasting fix.

Responsibilities

Build and operate AWS infrastructure using Terraform and Kubernetes, including EKS clusters, networking, IAM, load balancing, and secrets management.
Develop reusable infrastructure modules and consistent development, staging, and production environments.
Build deployment workflows with automated checks, clear deployment visibility, and reliable rollbacks.
Design autoscaling and graceful shutdown behavior for services handling long-running voice conversations.
Improve observability across infrastructure and application services, with useful dashboards, actionable alerts, and clear reliability and latency metrics.
Investigate production issues, improve incident response, and address recurring problems at their source.
Partner with application and AI engineers to deploy new services, improve performance, and manage infrastructure costs.
Make it easier for engineers to provision resources, deploy changes, and debug their services safely.