Senior DevOps AI Platform Engineer (Remote, NC, US, 99999)

Continue to Grow with GXO. At GXO, we know our greatest asset is people like you - energetic, innovative people of all experience levels and talents who make GXO a great place to work. Your career matters to us because your passion and excitement will help keep our company moving forward. Senior DevOps AI Platform Engineer Are you ready to take your career to the next level with a rapidly growing global company? As a Senior DevOps Platform Engineer, you will establish and scale the enterprise DevOps operating model for GXO's Agentic AI Platform. This role is responsible for building secure, automated, and scalable cloud platform capabilities that enable AI application delivery across Google Cloud Platform, Kubernetes, Terraform, and CI/CD ecosystems. You'll partner closely with Cloud Engineering, Platform Architecture, Security, and Product teams to drive developer productivity, platform reliability, and operational excellence. If you're looking for an opportunity to make a significant impact on enterprise AI infrastructure, join us at GXO. Pay, benefits and more We are eager to attract the best, so we offer competitive compensation and a generous benefits package, including full health insurance (medical, dental and vision), 401(k), life insurance, disability and the opportunity to participate in a company incentive plan. What you'll do on a typical day Establish the enterprise DevOps operating model for GXO's Agentic AI Platform, including CI/CD standards, branching strategies, release governance, environment promotion, deployment approvals, and operational handoff practices. Design, build, and manage secure, repeatable CI/CD pipelines supporting AI platform infrastructure, platform services, agents, MCP servers, LiteLLM, Agent Gateway integrations, model-serving components, and supporting services. Engineer, deploy, and operate Kubernetes-based platform capabilities on Google Kubernetes Engine (GKE), including deployment standards, Helm or Kustomize, autoscaling, network policies, workload identity, secrets management, ingress/egress, observability, and production runbooks. Own Terraform infrastructure delivery by developing reusable modules, managing state, enforcing pull request controls, implementing policy guardrails, maintaining environment parity, detecting configuration drift, and promoting infrastructure across development, test, staging, and production environments. Partner with the Principal Cloud Engineer to implement Google Cloud Platform foundations while leading day-to-day DevOps enablement, release engineering, Kubernetes operations, pipeline reliability, and developer experience. Collaborate with the Principal Cloud AI Platform Architect to translate enterprise architecture standards, reference architectures, and architectural decision records (ADRs) into automated build, test, deployment, and operational processes. Enable Phase 2 platform capabilities, including GKE-based open-source model serving, vLLM or comparable inference runtimes, scalable deployment patterns, model tiering infrastructure, and cost-governed platform operations. Implement enterprise DevSecOps controls in partnership with Information Security, including vulnerability scanning, dependency scanning, container image hardening, Binary Authorization (or equivalent), secrets management, audit logging, and secure deployment gates. Create standardized "paved road" developer workflows that enable engineers to provision environments, deploy AI agents, publish MCP services, test integrations, and promote code changes through approved automation. Champion AI-assisted software engineering practices by enabling secure AI coding tools, automated testing, documentation generation, code review acceleration, pipeline diagnostics, and developer productivity improvements. Build comprehensive observability across the platform through logs, metrics, traces, dashboards, alerts, SLOs, SLIs, deployment health monitoring, traceability, cost attribution, and operational readiness reporting. Automate operational processes to reduce manual effort, improve incident response readiness, and maintain runbooks for releases, rollbacks, break-glass procedures, platform operations, and escalation processes. Support secure integration between the AI platform and Snowflake-governed data access patterns through automated deployment, configuration, policy enforcement, and runtime observability. Develop and maintain engineering documentation, including CI/CD standards, Terraform module guidance, Kubernetes operating procedures, release checklists, onboarding documentation, and operational runbooks. What you need to succeed at GXO At a minimum, you'll need Bachelor’s degree in computer science, Engineering, Information Technology, Cloud Computing, or a related technical field; equivalent hands-on experience may be considered. Google Cloud Professional DevOps Engineer certification required. Minimum of 8 years of platform engineering, DevOps, Site Reliability