ashby
ML Ops / Dev Ops Engineer
Zensors
San Francisco, USA
Full-time
Mid · 4+ years experience
Onsite
Discovered 1 weeks ago
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Role Overview
As an ML / DevOps Engineer, you will play a pivotal role in advancing our infrastructure, scaling enterprise deployment workflows, and refining automation architectures to enable rapid iteration across the organization. You will sit at the critical intersection of machine learning and systems engineering. This role requires deep technical expertise not just in cloud-native tools, but also in the foundational Linux systems and networking required to process high-throughput video data reliably and securely across both cloud and edge environments.
Key Skills for This Role
KubernetesHelmJenkinsGitLab CITerraformAnsible
Full Job Posting
About the Role
As an ML / DevOps Engineer, you will play a pivotal role in advancing our infrastructure, scaling enterprise deployment workflows, and refining automation architectures to enable rapid iteration across the organization. You will sit at the critical intersection of machine learning and systems engineering. This role requires deep technical expertise not just in cloud-native tools, but also in the foundational Linux systems and networking required to process high-throughput video data reliably and securely across both cloud and edge environments.
Key Responsibilities
- Infrastructure & Automation Strategy: Drive the design and implementation of automated infrastructure deployment and validation workflows supporting our cutting-edge AI and computer vision initiatives.
- Video Pipeline Operations: Design, optimize, and manage the infrastructure specifically tailored for ingesting, processing, and analyzing real-time video streams at scale. You will ensure high throughput, low latency, and rock-solid reliability for critical CV workloads.
- Systems & Networking Core: Maintain a strong systems foundation by managing high-performance Linux environments. You will architect and troubleshoot complex networking configurations (both cloud and edge) necessary for seamless video data transmission between physical cameras, processing nodes, and the cloud platform.
- Kubernetes & Orchestration: Create resilient automation pipelines, orchestrate complex Kubernetes-based environments, and ensure the seamless integration of diverse ML and software components.
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