Senior DevOps Engineer, AI Platform

GXO Logistics, Inc.

Senior DevOps Engineer establishing secure and scalable AI infrastructure at GXO. Collaborating with teams to enhance AI application delivery using the latest technology.

Posted 7/24/2026full-timeHouston • Texas • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in establishing and managing CI/CD pipelines, Kubernetes operations, and Terraform infrastructure delivery within Google Cloud Platform. Proficient in implementing DevSecOps practices and collaborating with cross-functional teams to enhance developer experience and operational readiness.

Highest-signal resume keywords

Google Cloud Professional DevOps Engineer CertificationGoogle Kubernetes Engine (GKE) ExpertiseTerraform Infrastructure ManagementCI/CD Pipeline Design and MaintenanceDevSecOps Implementation

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Applicant Tracking System Keywords

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Hard Skills

CI/CD StandardsKubernetes OperationsTerraform ModulesCloud BuildGitHub ActionsGitLab CIAzure DevOpsHelmKustomizeIncident Response

Soft Skills

Technical CommunicationCollaborationInfluencing Engineering TeamsOperational MindsetRoot Cause Analysis

Tools & Technologies

Google Cloud PlatformAgentic AI PlatformMCP ServersLiteLLMAgent GatewayObservability ToolsAutomation StandardsDeveloper WorkflowsSecurity GuardrailsDeployment Approvals

Certifications & Qualifications

Google Cloud Professional DevOps Engineer

Industry Keywords

DevOpsSite Reliability Engineering (SRE)Infrastructure EngineeringCloud EngineeringSoftware Delivery EngineeringAI PlatformMachine LearningData PlatformCloud-NativeProduction Environments

Tech Stack

Tools & technologies

AzureCloudGoogle Cloud PlatformJenkinsKubernetesTerraform

About the role

Key responsibilities & impact

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
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

Requirements

What 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 Engineering (SRE), infrastructure engineering, cloud engineering, or software delivery engineering experience
Minimum of 5 years of hands-on Google Cloud Platform experience supporting production environments
Deep expertise with Google Kubernetes Engine (GKE), including Kubernetes operations, workload identity, networking, autoscaling, ingress/egress, Helm or Kustomize, and production troubleshooting
Expert-level experience developing and managing Terraform infrastructure, including reusable modules, state management, CI/CD integration, policy-as-code, infrastructure promotion, and drift management
Strong experience designing and maintaining secure CI/CD pipelines using Cloud Build, GitHub Actions, GitLab CI, Azure DevOps, Jenkins, or similar platforms
Experience implementing GitOps and DevSecOps practices, including code review automation, dependency scanning, container security, secrets management, signed artifacts, deployment approvals, and security guardrails
Experience supporting cloud-native AI, machine learning, analytics, developer platform, or data platform workloads on Kubernetes and Google Cloud
Ability to collaborate effectively with principal architects, cloud engineers, Information Security, product teams, and software developers to translate architectural vision into production-ready solutions
Strong operational mindset with experience supporting incident response, root cause analysis, observability, production support, SLOs/SLIs, release readiness, and continuous operational improvement
Excellent technical communication skills with the ability to develop engineering documentation, operating procedures, automation standards, and developer guidance
Ability to influence engineering teams across a global matrix organization while driving adoption of modern DevOps and platform engineering practices

Benefits

Comp & perks

full health insurance (medical, dental and vision)
401(k)
life insurance
disability
opportunity to participate in a company incentive plan