Google Cloud Platform Jobs

We are looking for a skilled freelancer/server consultant with strong experience in Linux servers and AWS cloud infrastructure to help us build and optimize a parallel video clip rendering system. Project Overview: We run a video processing platform where multiple users generate video clips at the same time. We need expert support to design and configure a high-performance, scalable, and stable server setup for smooth parallel rendering. Required Skills: Strong experience with Linux server administration Expertise in AWS cloud services Experience with FFmpeg and video processing pipelines Knowledge of parallel processing (workers, queues, Celery, etc.) Server performance tuning & optimization Load balancing and resource management

I’m a hands-on DevOps engineer who specialises in Infrastructure as Code across AWS, Azure and Google Cloud Platform. I would rather focus on delivery than spend hours hunting and bidding for work, so I’m looking for someone who can take over the day-to-day bid process on my personal Freelancer profile. Your mission is straightforward: spot suitable DevOps jobs (especially those centred on IaC), craft compelling proposals that reflect my skills, submit them in a timely manner and keep me updated. Whenever a proposal you place is awarded, you earn a 20 % commission from that project’s value—paid immediately after the client’s first milestone clears. Key deliverables: • Monitor the marketplace daily and shortlist matching Infrastructure-as-Code projects o...

Design and operate a production-grade Google Cloud platform for a global SaaS solution with deep hands-on ownership of Kubernetes, CI/CD, service mesh, and auditability. Deploy and manage regional GKE clusters (e.g., primary in me-central1/Riyadh), implement secure multi-environment landing zones, and build reusable Infrastructure-as-Code with automated, auditable provisioning. Build CI/CD pipelines using Cloud Build and/or GitHub Actions to containerize and deliver Python backend and Angular frontend services, publish Docker images, and deploy via Helm charts with tenant-specific configuration. Implement advanced deployment strategies on GKE using Istio service mesh, including header-based canary releases and tenant-aware traffic routing (e.g., X-Tenant-ID). Enable global scalability, mul...

I’m moving a full-stack Node.js / TypeScript application (Express API plus a React-Vite front end) from Replit into a secure, production-grade Google Cloud environment. Everything will be green-field, so you can design the layout cleanly without wrestling with legacy resources. Only the production tier is required. WHAT I NEED YOU TO BUILD • A GitHub-triggered Cloud Build pipeline that containerises the app, pushes to Artifact Registry, and rolls out to Cloud Run with zero-downtime updates. • Private Cloud SQL for PostgreSQL (Private IP via Private Service Access) reachable from Cloud Run through a Serverless VPC Access connector. • Secrets (DB creds, JWT keys, etc.) stored in Secret Manager and injected at runtime. • External HTTPS Load Balancer fronting...

I need an expert who can review the current implementation and make sure the widget is working Your task will be to audit the existing code and adjust the way to make it work

I am looking for a tutor who can help me understand, in a comprehensive and step-by-step manner, how to deploy a production-ready dashboard that displays the results of a model. My current stack includes: Google Cloud Platform (GCP) Vertex AI Workbench (where I trained the model) Streamlit (for the dashboard) Git and GitHub (for version control) I already have: A trained model in Workbench The .py files developed A structured and synchronized GitHub repository The intention to deploy everything in GCP However, I am not only looking for help with deployment — I want to fully understand the technical process behind it, including: How to properly structure the project for production. How to connect the trained model to the dashboard. The best architecture choice in GCP for...