About the job

The Google Cloud Consulting Professional Services team guides customers through the moments that matter most in their cloud journey to help businesses thrive. We help customers transform and evolve their business through the use of Google’s global network, web-scale data centers, and software infrastructure. As part of an innovative team in this rapidly growing business, you will help shape the future of businesses of all sizes and use technology to connect with customers, employees, and partners.

As a Cloud Platform and Infrastructure Engineer, you will provide technical guidance to customers adopting Google Cloud Platform (GCP) services. This includes providing best practices on secure foundational cloud implementations, automated provisioning of infrastructure and applications, cloud-ready application architectures, and more. You will also provide guidance in ensuring that customers receive the best of what GCP can offer and have the best experience in migrating, building, modernizing, and maintaining applications in GCP. Additionally, you will work with Product Management and Product Engineering to drive excellence in Google Cloud products and features.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

The US base salary range for this full-time position is $153,000-$222,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Propose solution architectures and manage the deployment of cloud networking solutions according to customer requirements and implementation best practices.

• Work with customers to design and implement Cloud based technical architectures, migration approaches, and application optimizations that enable business objectives.

• Work with internal Specialists, Product, and Engineering teams to package approaches, best practices, and lessons learned into thought leadership, methodologies, and published assets.

• Interact with sales, partners, and customer technical stakeholders to manage project scope, priorities, deliverables, risks and issues, and timelines.

• Travel up to 30% for in-region for meetings, technical reviews, and onsite delivery activities.

Minimum qualifications

• Bachelor's degree in Computer Science or equivalent practical experience.

• 6 years of experience automating infrastructure provisioning, Developer Operations (DevOps), continuous integration, or delivery utilizing Kubernetes and Linux-based systems.

• 3 years of experience in project management and technical solution delivery.

• Experience coding in one or more general purpose languages (e.g., Python, Java, Go, C or C++) including data structures, algorithms, software design, Linux environments and Kubernetes orchestration.

• Experience working with Cloud Providers such as Google Cloud Platform (GCP).

• Ability to travel 30% of the time, as needed, for client engagements.

Preferred qualifications

• Experience with third-party networking (e.g., PANW, Fortinet, VMWare) and design, including redundancy and load balancing.

• Experience troubleshooting networking protocols including TCP/IP, Hypertext Transfer Protocol, and Border Gateway Protocol (BGP).

• Experience in customer-facing migration, including service discovery, assessment, planning, execution, and operations.

• Experience with standard IT security practices, including IAM, data protection, encryption, and certificate/key management.

• Experience running AI/ML training and inference workloads on GPU/TPU using frameworks such as Py Torch, JAX, Tensor Flow, or Slurm.

• Knowledge of containerization and orchestration technologies, including Google Kubernetes Engine (GKE) and related cloud-native services.