Staff DevOps Engineer

present of AI; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

present of AI is seeking a hands-on platform engineer to run cloud infrastructure at scale in San Francisco. You will manage Kubernetes clusters across two clouds, implement Terraform across many accounts, and own CI/CD pipelines with built-in security.

The role emphasizes internal tools using LLM APIs for faster platform operations, incident triage, drift detection, and automation, while keeping the company’s AI security product reliable and auditable.

Qualifications

• Experience running cloud infrastructure at scale.
• Strong hands-on with Kubernetes clusters across multiple regions.
• Proficiency with Terraform and CI/CD pipelines.

Responsibilities

• Own platform infrastructure operations at scale in SF.
• Build internal tools around LLM APIs to speed up platform work.
• Triage incidents and implement drift detection and automated remediation.

Skills

Kubernetes

Go

Python

AWS

GCP

CI/CD

Terraform

LLM APIs

Observability

Tools

Terraform

OpenTelemetry

Job description

This is a hands on platform role for someone who has run cloud infrastructure at real scale for a decade or more. The work centers on many Kubernetes clusters across two clouds, Terraform across many accounts and regions, and CI/CD pipelines with security built in. A strong candidate brings deep AWS knowledge, working GCP experience, solid Go or Python for internal tooling, and the habit of taking a vague problem through to a running production system alone.

How it fits the AI era

AI shows up in two ways here. The role asks for internal tools built on LLM APIs to help with platform operations, incident triage, drift detection, and pipeline automation, so part of the job is making the platform team faster with AI. The other part is keeping the company's own AI security product running reliably, which still depends on human judgment about architecture, risk, and tradeoffs.

How it builds AI experience

A person in this role would gain direct practice wiring LLM APIs into engineering workflows and seeing where they help and where they fail. Because the company builds AI security and governance, there is also exposure to how AI activity is observed and controlled at the network level. Building that background means shipping at least one real internal tool on an LLM API and learning the security and reliability limits firsthand.

Worth showing

• Terraform at scale
• Go or Python tooling
• LLM API internal tools
• Observability with OpenTelemetry

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About WitnessAI

WitnessAI provides a unified AI security and governance platform that observes, controls, and protects AI activity across employees, models, applications, and agents, per the company.