Position OverviewWe 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. The ideal candidate will have strong expertise in cloud and on-premises infrastructure, Kubernetes, containerization, microservices, MLOps, CI/CD automation, and production operations.
This role requires an engineer who can partner closely with Product, Engineering, Data Science, AI and IT Security teams to design, deploy, scale, secure, and maintain mission-critical applications while ensuring operational excellence and adherence to DevOps best practices.
The position includes responsibility for production support, infrastructure automation, platform reliability, and continuous improvement of deployment and operational standards.
Key ResponsibilitiesPlatform 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.
- Collaborate with Data Science and AI Engineering teams to operationalize machine learning models.
- Implement MLOps practices including model deployment, versioning, monitoring, rollback strategies, and lifecycle management.
- Utilize AIOps techniques for proactive monitoring, anomaly detection, incident response, and operational optimization.
- Assist in performance tuning and resource optimization of AI/ML applications.
- Evaluate, customize, and deploy AI coding agents (e.g., Cursor, Claude Code, Codex) tuned to Coparts's monorepo, conventions, and internal libraries.
- Build custom agents for tasks.
- Own and operate the end-to-end internal AI stack from model selection and integration to deployment and monitoring.
Application Deployment & Operations- Deploy and support Python-based , Java-based applications and AI services.
- Build and manage workflows using tools such as n8n and related automation platforms.
- Troubleshoot deployment, performance, scalability, and reliability issues across distributed systems.
- Maintain highly available production environments with a focus on uptime, security, and performance.
- Develop operational runbooks, deployment documentation, and support procedures.
- Build and maintain custom AI agents and LLM-powered tools tailored to Copart's engineering workflows.
Production Support & Reliability- Provide day-to-day support for production applications and infrastructure.
- Participate in release activities, maintenance windows, upgrades, and production deployments, including support during extended hours when required.
- Perform root cause analysis (RCA) and implement preventive measures to reduce recurring incidents.
- Monitor system health, performance, capacity, and availability.
- Collaborate with engineering teams to improve observability, alerting, and operational readiness.
Collaboration & Requirements Gathering- Work closely with Product Managers, Architects, Software Engineers, Data Scientists, and Business Stakeholders.
- Participate in requirements 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 Qualifications- 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:
- Kubernetes (on-premises and cloud)
- Docker containerization
- Linux system administration
- Python application deployment and operations
- CI/CD pipeline development and automation
- Infrastructure automation and configuration management
- Experience deploying and supporting AI, ML, or Agentic applications in production.
- Experience operating GPU-based infrastructure for AI workloads.
- Strong understanding of MLOps concepts and practices.
- Experience with AI model deployment, monitoring, and operational support.
- Experience supporting production releases, maintenance activities, and incident management.
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