Senior DevOps Engineer / Platform Engineer

Copart•Dallas, TX

About The Position

Copart, Inc. is 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. We are seeking a highly skilled Senior DevOps Engineer / Platform Engineer with strong expertise in Linux systems, Kubernetes, cloud-native platforms, automation, infrastructure engineering, and production operations. The ideal candidate will have extensive experience designing, deploying, operating, and supporting large-scale enterprise applications across cloud and on-premises environments. This role will focus primarily on platform engineering, Kubernetes administration, CI/CD automation, Infrastructure as Code, site reliability, production support, and operational excellence. In addition to Kubernetes, Linux, and cloud platforms, the role includes responsibility for supporting middleware technologies, messaging platforms, web servers, and application hosting environments including Kafka, Tomcat, and NGINX-based services. Exposure to AI/ML and modern AI application platforms is desirable. The position requires close collaboration with Software Engineering, Architecture, Security, Product, and Infrastructure teams to ensure applications and platforms meet organizational standards for reliability, security, scalability, and operational support.

Requirements

5+ years of experience in DevOps, Platform Engineering, Site Reliability Engineering, Systems Engineering, or Infrastructure Engineering.
Strong hands-on experience administering Linux-based production environments.
Extensive experience with Kubernetes administration and container platforms.
Strong knowledge of Docker and cloud-native application architectures.
Experience building and operating CI/CD pipelines.
Experience with Infrastructure as Code tools such as Terraform.
Experience with configuration management and automation frameworks such as Ansible.
Strong troubleshooting skills across infrastructure, operating systems, networking, and distributed applications.
Strong experience supporting Spring Boot-based applications, including deployment, monitoring, and troubleshooting, is required.
Experience supporting enterprise production environments and critical business services.
Experience with monitoring, observability, alerting, and incident management practices.
Strong scripting and automation skills using Python, Bash, or similar languages.
Hands-on experience supporting Apache Kafka or enterprise messaging platforms.
Experience supporting Apache Tomcat and Java-based application deployments.
Experience administering and troubleshooting NGINX and/or Apache web servers.
Strong understanding of load balancing, reverse proxy architectures, SSL/TLS, and web application delivery.
Experience troubleshooting distributed applications across infrastructure, middleware, messaging, and application layers.

Nice To Haves

Exposure to AI/ML and modern AI application platforms is desirable.
Experience supporting AI/ML applications in production environments.
Experience with GPU-based infrastructure.
Familiarity with MLOps concepts and practices.
Knowledge of AI application deployment patterns, including LLM- and RAG-based systems
Relevant cloud or Kubernetes certifications.

Responsibilities

Design, build, automate, and maintain enterprise DevOps and platform engineering solutions supporting business-critical applications.
Deploy, manage, and operate Kubernetes platforms across cloud and on-premises environments.
Build and maintain containerized application platforms using Docker and Kubernetes.
Develop and maintain Infrastructure as Code solutions for scalable, repeatable, and secure deployments.
Define and implement platform standards, deployment frameworks, operational processes, and DevOps best practices.
Automate infrastructure provisioning, environment management, application deployments, and operational workflows.
Support platform modernization initiatives and cloud-native architecture adoption.
Collaborate with architecture and engineering teams to improve platform scalability and operational efficiency.
Administer and support large-scale Linux environments.
Perform advanced Linux system troubleshooting, performance tuning, and capacity planning.
Manage system upgrades, maintenance activities, and lifecycle management processes.
Develop automation scripts to improve operational efficiency and reduce manual intervention.
Support infrastructure security and compliance initiatives.
Design, implement, and maintain CI/CD pipelines supporting enterprise application deployments.
Automate build, testing, deployment, rollback, and release management processes.
Integrate security, compliance, and quality controls into software delivery workflows.
Improve deployment reliability through automation and standardized release procedures.
Partner with development teams to optimize software delivery processes and reduce deployment risks.
Support highly available production environments and critical business applications.
Participate in release activities, maintenance windows, platform upgrades, and production deployments, including extended-hours support when required.
Perform incident response, troubleshooting, root cause analysis, and problem management.
Develop operational runbooks, recovery procedures, and support documentation.
Implement monitoring, alerting, logging, and observability solutions.
Drive continuous improvement initiatives focused on reliability, availability, and operational excellence.
Collaborate with engineering teams to improve application resiliency and operational readiness.
Manage infrastructure across AWS, GCP, and hybrid-cloud environments.
Implement and maintain networking, load balancing, storage, and security solutions.
Support infrastructure scalability, performance optimization, and cost management initiatives.
Ensure infrastructure environments adhere to organizational security and compliance standards.
Support deployment and operation of AI/ML workloads running on Kubernetes-based platforms.
Assist engineering teams with infrastructure requirements for machine learning and AI applications.
Support GPU-enabled infrastructure for model training and inference workloads.
Contribute to MLOps initiatives such as deployment automation, monitoring, and operational support.
Apply familiarity with LLM-based or AI-powered applications when relevant to platform requirements.