Senior DevOps/MLOps Engineer

#### **Engineering** **Senior DevOps/MLOps Engineer** ================================ Remote Full\-Time **Job Description** ------------------- **Elevator Pitch** ------------------ Leverege is hiring a Senior DevOps/MLOps Engineer. We build AI\-native software that turns cameras into real\-time visibility into physical operations, and our computer vision runs everywhere from managed Kubernetes in the cloud to a growing fleet of on\-site edge servers in auto service centers, factories, and stores. This role owns the infrastructure that keeps all of it running and builds the systems that let us deploy and manage that edge fleet at scale. This is a rare chance to join a high\-trust, fully remote company and own platform and MLOps work that directly moves the business. If you love Kubernetes, think in terms of fleets rather than single servers, and want to own the GPU and edge infrastructure that gets computer vision to customers reliably, we would love to hear from you! **The Opportunity** ------------------- Leverege builds VisionAI software that helps businesses see what is happening across their physical operations. We connect to cameras, run proprietary computer vision models (often on a small edge appliance on\-site), and deliver real\-time operational insights to Fortune 500 customers across automotive service, manufacturing, and retail. Our products run on the Leverege Stack across per\-customer environments in Google Cloud, with GPU and inference infrastructure supporting our machine learning team and a fleet of edge servers deployed at customer locations. That footprint is scaling fast, from dozens of deployments toward hundreds, and the infrastructure needs to scale with it. Today, a lot of edge server provisioning and management is manual. We need someone to own the platform, harden it, and build the automation that makes deploying and updating a large edge fleet routine and safe. You will be a senior individual contributor on the DevOps team. Success means our products ship reliably, our GPU and inference infrastructure keeps up with the ML team, incidents are rare and quickly resolved, and managing hundreds of edge servers feels as controlled as managing one. **What You’ll Do** ------------------ You will own the infrastructure that runs Leverege’s VisionAI in production, in the cloud and at the edge, and build the systems that let us operate it at scale. * **Own and operate the GKE platform** across our per\-customer Google Cloud projects, including provisioning new customer clusters end to end with Terraform, Helm, and GitOps. * **Build the edge fleet management systems** that let us deploy, monitor, update, and roll back software across a growing fleet of on\-site edge servers, replacing manual per\-server work with automated, auditable processes. * **Run the MLOps path for computer vision** by building repeatable pipelines that get GPU workloads and CV models from the ML team onto inference nodes and the edge