> Markdown version of [/jobs/ext/2025314-cloud-infrastructure-engineer](https://www.wearedevelopers.com/jobs/ext/2025314-cloud-infrastructure-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Cloud Infrastructure Engineer - **Company:** CATCH Inc. - **Location:** United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Bash Shell, Cloud Computing, Cloud Storage, Continuous Delivery, Continuous Integration, Linux, Distributed Computing Environment, Distributed Data Store, Distributed Systems, Field-Programmable Gate Array (FPGA), Github, Python (Programming Language), Machine Learning, Network Architecture, Network File Systems, Reliability Engineering, Software Engineering, Scripting, Google Cloud, Cloud Platform System, High Performance Computing, Build Server, Kubernetes, Infrastructure Automation Frameworks, Storage Technologies, Information Technology, Slurm, Terraform - **Published:** August 11, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-cloud-infrastructure-engineer-ny--92abb22a-5908-45ea-aafe-b32b077fb637 ## About the Role * A bachelor's degree in Computer Science or a related field. * At least 5 years of relevant experience in Google Cloud Platform (GCP) infrastructure or related engineering roles. * Proficiency in Bash, Python, or Go for automation. * Strong expertise in HPC and distributed workloads to design scalable, high-performance cloud infrastructure. * Deep hands-on experience with Infrastructure as Code (Terraform, Google Cloud Infrastructure Manager), Linux systems, cloud networking, and storage architectures. * Production experience automating infrastructure workflows and building complex CI/CD pipelines using Cloud Build, Cloud Deploy, GitHub Actions, etc. * Ability to build automated, production-grade cloud platforms for compute-intensive environments. * Deep understanding of cloud networking, storage architectures, and storage protocols (NFS, object, block, parallel/distributed storage). * Expertise with Google Kubernetes Engine (GKE) or Kubernetes at scale. * Experience with HPC schedulers such as Slurm or HTCondor and distributed computing frameworks such as Ray. * Eligibility to work in the US for any employer (sponsorship is not available)., Automation, Bash Scripting, Business Process Outsourcing, Cloud Computing, Cloud Storage, Computer Science, Computer Systems, Consulting, Continuous Deployment/Delivery, Continuous Integration, Contract Processing, Digital Media, Distributed Computing, FPGA, Fitness, GCP (Good Clinical Practices), GitHub, Linux Operating System, Machine Learning, NFS (Network File System), Network Architecture/Engineering, Problem Solving Skills, Professional Services, Python Programming/Scripting Language, Recruiting/Staffing Agency, Reliability Engineering, Software Engineering, Sports, Storage Architecture, Team Player, Technical Recruiting ## Description * Work for a stable, high-growth organization that actively supports personal and professional development. * Leverage deep expertise in cloud infrastructure, HPC, and distributed systems in ways that directly shape trading performance and platform capability. * Solve genuinely hard technical problems in low-latency infrastructure, Kubernetes, and large-scale cloud migrations alongside world-class engineers. * Enjoy a stimulating, results-oriented environment with a collaborative culture, no egos, and a team that celebrates great ideas. WHAT YOU'LL DO * Architect and implement scalable cloud infrastructure using Terraform, Google Cloud Infrastructure Manager or equivalent Infrastructure-as-Code tools. * Design high-performance compute environments, including optimized networking and storage stacks. * Manage and optimize Kubernetes clusters in cloud environments. * Automate infrastructure workflows and build complex CI/CD pipelines. * Build and maintain custom cloud images. * Implement robust monitoring and observability for performance-critical workloads. * Lead on-prem to cloud and cloud-to-cloud migrations of HPC and distributed workloads. * Collaborate with research and engineering teams to optimize compute, storage, and scheduling efficiency. ## Related Videos - [Running Secure Life Science Research at Scale using Hybrid GPU HPC and Kubernetes 🧬](https://www.wearedevelopers.com/videos/100355-running-secure-life-science-research-at-scale-using-hybrid-gpu-hpc-and-kubernetes) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Docker network without Docker](https://www.wearedevelopers.com/videos/1418-docker-network-without-docker) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) - [Docker exec without Docker](https://www.wearedevelopers.com/videos/1094-docker-exec-without-docker) ## Related Articles - [7 Cloud Computing Trends Coming in 2025 for Developers](https://www.wearedevelopers.com/magazine/412-7-cloud-computing-trends-coming-in-2025-for-developers) - [Got AI ideas but no money? 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