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ML/Dev Ops Systems Engineer
Zensors
San Francisco, USA
Full-time
Senior · 6+ years experience
Onsite
Discovered 1 weeks ago
LinuxKubernetesHelmJenkinsGitLab CIArgoCD
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Key Responsibilities
- Infrastructure & Automation Strategy: Drive the design and implementation of automated infrastructure deployment and validation workflows supporting our 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 reliability for critical computer vision 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 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 software components.
- CI/CD & Deployment: Design sophisticated CI/CD pipelines. Your scope will include automating infrastructure provisioning (potentially bare-metal-to-Kubernetes bring-up), deploying microservices utilizing Helm, and integrating security scans and static code analysis tools into the workflow.
- Reliability & Monitoring: Build comprehensive monitoring systems and automated alerting mechanisms tailored for AI/video workloads. You will diagnose and resolve complex build failures and production issues related to system resources or network bottlenecks.
- Collaboration & Scaling: Collaborate deeply with ML engineers to ensure validation readiness for new models and take ownership of scaling enterprise deployment workflows across the organization.
Qualifications
- Professional Profile: A highly motivated and passionate professional with deep expertise in DevOps, automation engineering, Linux systems internals, advanced networking, and infrastructure orchestration. You must have a strong track record of technical execution, complex systems integration, and successful cross-team collaboration.
- Education & Experience: A BS, MS, or PhD in Computer Science or a related equivalent field, combined with 6+ years of applicable industry experience in DevOps or Systems Engineering.
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