Senior DevOps Engineer

Job summary

a technology leader and the premier online vehicle auction platform globally, with over 200 facilities located across the world, Copart links vehicle sellers to more than 750,000 buyers in over 190 countries. We believe in providing an unmatched experience, every day and everywhere, driven by our people, processes, and technology.

Qualifications

• We believe in providing an unmatched experience, every day and everywhere, driven by our people, processes, and technology.
• We are seeking a highly skilled Mid-Level to Senior DevOps Engineer with hands-on experience building, deploying, operating, and supporting modern AI, Machine Learning, and Agentic applications.
• Gathering
• Work closely with Product Managers, Architects, Software Engineers, Data Scientists, and Business Stakeholders.
• Participate in
• gathering, solution design, and infrastructure planning.
• Ensure applications and platforms are built according to organizational DevOps, security, reliability, and operational standards.
• Establish and maintain deployment, monitoring, and support standards across teams.
• Required
• 5+ years of experience in DevOps, Platform Engineering, Site Reliability Engineering (SRE), or Infrastructure Engineering roles.
• Proven experience supporting production applications in enterprise environments.
• Strong hands-on experience with:

Responsibilities

• The position includes responsibility for production support, infrastructure automation, platform reliability, and continuous improvement of deployment and operational standards.
• Platform Engineering & DevOps
• Design, build, automate, and maintain DevOps platforms supporting AI, ML, Agentic, and traditional applications.
• Deploy, containerize, and manage applications using Docker and Kubernetes across both on-premises and cloud environments.
• Develop Infrastructure as Code (IaC) solutions for repeatable, scalable, and secure deployments.
• Build and maintain CI/CD pipelines that support rapid and reliable application releases.
• Define and implement DevOps standards, deployment frameworks, operational procedures, and platform best practices.
• Automate environment provisioning, application deployment, monitoring, and operational workflows.
• AI / MLOps / AIOps
• Deploy, manage, and optimize AI/ML workloads in production environments.
• Support LLM-based, Agentic AI, Retrieval-Augmented Generation (RAG), and AI workflow platforms.
• Manage and optimize GPU-based infrastructure for AI training and inference workloads.

Degrees

Work schedule

Industry

Company size