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Google Devops Jobs in Houston, TX (NOW HIRING)

Jobtailor

Senior DevOps Engineer, AI Platform

Houston, TX · On-site

$150 - $210/hr

Engineer, deploy, and operate Kubernetes-based platform capabilities on Google Kubernetes Engine ... Automate operational processes to reduce manual effort, improve incident response readiness, and ...

New

Cimarron

DevOps Engineer

Houston, TX · On-site

$100K - $165K/yr

Posting/External Job Title DevOps Engineer Location Houston, TX 77058 US (Primary) Remote/Onsite ... Experience with cloud platforms such as AWS or Google Cloud Platform. * Proficiency with Kubernetes ...

TMW Merchants LLC

Sr DevOps Engineer Intake

Houston, TX · On-site

$120 - $140/hr

Sr DevOps Engineer - Tailored Brands We currently have an exciting opportunity for Sr DevOps ... Hands-on experience with Google Cloud Platform (GKE, Cloud Run, Cloud Functions, Cloud SQL, Pub/Sub ...

New

Tailored Brands

Sr DevOps Engineer Intake

Houston, TX · On-site

$134K - $172K/yr

What you'll bring • 8+ years of experience in DevOps, SRE, or cloud infrastructure engineering. • Hands-on experience with Google Cloud Platform (GKE, Cloud Run, Cloud Functions, Cloud SQL, Pub ...

Deloitte

Google AI Architect

Houston, TX · On-site

Google AI Architect/AI and Engineering Join our AI & Engineering team in transforming technology ... Engineering as a Service provides complete design, implementation, and technology operations ...

Patterson-UTI

Senior Site Reliability Engineer NEX

Houston, TX · On-site

$54.50 - $72.25/hr

... or DevOps. 3+ years of experience operating production workloads in Google Cloud Platform. Ability to understand and communicate in English at a level sufficient to issue, receive, and respond to ...

Deloitte

Google AI Lead Architect

Houston, TX

$52.75 - $72.25/hr

Google AI Lead Architect/AI & Engineering: Join our AI & Engineering team in transforming ... Engineering as a Service provides complete design, implementation, and technology operations ...

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How much do google devops jobs pay per hour?

As of Aug 20, 2026, the average hourly pay for google devops in Houston, TX is $57.81, according to ZipRecruiter salary data. Most workers in this role earn between $48.41 and $66.35 per hour, depending on experience, location, and employer.

What is a Google DevOps?

A Google DevOps job involves managing and automating the deployment, monitoring, and scaling of applications and infrastructure on Google Cloud. DevOps engineers at Google work to streamline development workflows, improve system reliability, and implement best practices for CI/CD and site reliability engineering. They collaborate with developers, operations, and security teams to ensure seamless application performance. Strong knowledge of cloud services, automation tools, and programming is essential for this role.

What are some common challenges faced by Google DevOps professionals in their day-to-day work?

Google DevOps professionals often encounter challenges such as maintaining high system reliability during frequent deployments and rapidly evolving infrastructure requirements. Balancing automation with security best practices, managing large-scale cloud resources, and ensuring seamless collaboration between development and operations teams are other common hurdles. Adapting quickly to new tools and processes, and relentlessly troubleshooting complex issues, are essential aspects of the role. Successfully navigating these challenges is key to supporting fast-paced product development while maintaining robust and secure applications.

What are the key skills and qualifications needed to thrive in the Google DevOps position, and why are they important?

To thrive as a Google DevOps professional, you need a solid background in cloud computing, automation, CI/CD pipelines, and infrastructure-as-code, usually backed by a degree in computer science or related certifications like Google Cloud Professional DevOps Engineer. Familiarity with tools such as Kubernetes, Terraform, Docker, and Google Cloud Platform services is essential for managing scalable and reliable systems. Strong collaboration, analytical thinking, and troubleshooting skills help drive cross-functional initiatives and resolve issues efficiently. These skills are crucial to maintaining system reliability, optimizing deployment processes, and supporting high-performing engineering teams.

Does Google hire DevOps?

Google hires DevOps engineers to work on infrastructure, automation, and deployment processes using tools like Kubernetes, Docker, and Google Cloud Platform. These roles typically require experience with cloud services, scripting, and continuous integration/continuous deployment (CI/CD) practices.

What are the most commonly searched types of Google Devops jobs in Houston, TX?

The most popular types of Google Devops jobs in Houston, TX are:

What are popular job titles related to Google Devops jobs in Houston, TX?

For Google Devops jobs in Houston, TX, the most frequently searched job titles are:

What job categories do people searching Google Devops jobs in Houston, TX look for?

The top searched job categories for Google Devops jobs in Houston, TX are:

What cities near Houston, TX are hiring for Google Devops jobs?

Cities near Houston, TX with the most Google Devops job openings:

Google Devops jobs near you
Infographic showing various Google Devops job openings in Houston, TX as of August 2026, with employment types broken down into 85% Full Time, 10% Part Time, and 5% Contract. Highlights an 73% Physical, 4% Hybrid, and 23% Remote job distribution, with an average salary of $120,239 per year, or $57.8 per hour.

Senior DevOps Engineer, AI Platform

Jobtailor

Houston, TX • On-site

$150 - $210/hr

Other

Posted 2 days ago

New

Job description

  • 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
  • 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
Core Competencies

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 Certification
  • Google Kubernetes Engine (GKE) Expertise
  • Terraform Infrastructure Management
  • CI/CD Pipeline Design and Maintenance
  • DevSecOps Implementation
ATS Optimization Keywords Hard Skills
  • CI/CD Standards
  • Kubernetes Operations
  • Terraform Modules
  • Cloud Build
  • GitHub Actions
  • GitLab CI
  • Azure DevOps
  • Helm
  • Kustomize
  • Incident Response
Soft Skills
  • Technical Communication
  • Collaboration
  • Influencing Engineering Teams
  • Operational Mindset
  • Root Cause Analysis
Certifications & Qualifications
  • Google Cloud Professional DevOps Engineer
Industry Keywords
  • DevOps
  • Site Reliability Engineering (SRE)
  • Infrastructure Engineering
  • Cloud Engineering
  • Software Delivery Engineering
  • AI Platform
  • Machine Learning
  • Data Platform
  • Cloud-Native
  • Production Environments
Tools & Technologies
  • Google Cloud Platform
  • Agentic AI Platform
  • MCP Servers
  • LiteLLM
  • Agent Gateway
  • Observability Tools
  • Automation Standards
  • Developer Workflows
  • Security Guardrails
  • Deployment Approvals
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