Looking for Senior Google Cloud Platform Engineer / Cloud Architect – ML & Cluster Infrastructure_ Remote

Job Description: Senior Google Cloud Platform Engineer / Cloud Architect – ML & Cluster Infrastructure

Position: Senior Google Cloud Platform Engineer / Cloud Architect

Experience: 8–10 years overall, with strong hands-on Google Cloud Platform experience

Role Overview

We are looking for a highly experienced Google Cloud Platform Engineer / Cloud Architect with strong Machine Learning (ML) expertise and extensive experience managing large-scale clusters and distributed infrastructure.

The ideal candidate must be technically strong, hands-on, and confident enough to take complete ownership of the cloud and ML infrastructure space. We need someone who can architect solutions, drive technical decisions, troubleshoot complex issues, and work independently without constant supervision.

This is a high-ownership role requiring someone who can be present, command the technical space, and drive outcomes end to end.

Key Responsibilities

  • Design, build, and manage scalable, secure, and highly available cloud architectures on Google Cloud Platform (Google Cloud Platform).
  • Lead the architecture and implementation of large-scale cluster environments using Google Kubernetes Engine (GKE) and related container orchestration technologies.
  • Build and optimize infrastructure supporting ML workloads, distributed training, model deployment, and inference pipelines.
  • Work extensively with Google Cloud Platform services such as Vertex AI, BigQuery, Cloud Storage, IAM, VPC, Cloud Monitoring, and Cloud Logging.
  • Manage cluster scalability, performance optimization, workload scheduling, resource utilization, networking, and reliability.
  • Collaborate with ML engineers and data science teams to operationalize machine learning models and productionize ML workloads.
  • Implement MLOps practices, including model deployment, CI/CD, model monitoring, automation, and infrastructure as code.
  • Own technical architecture, design decisions, production issues, root-cause analysis, and performance improvements.
  • Establish cloud security, governance, observability, cost optimization, and operational best practices.
  • Act as the go-to technical authority for Google Cloud Platform, cluster infrastructure, and ML platform-related challenges.
Required Technical Skills

Google Cloud Platform (Google Cloud Platform)

  • Strong hands-on experience designing and implementing enterprise-grade Google Cloud Platform architectures.
  • Expertise in Google Cloud Platform networking, IAM, security, compute, storage, and monitoring.
  • Experience with infrastructure automation using Terraform and CI/CD tools.
Kubernetes & Cluster Engineering
  • Extensive experience with GKE, Kubernetes, Docker, and containerized workloads.
  • Proven experience managing large-scale clusters, distributed workloads, autoscaling, and resource optimization.
  • Strong understanding of cluster networking, observability, high availability, and troubleshooting.
Machine Learning & MLOps
  • Strong understanding of ML workflows, model training, deployment, and inference.
  • Hands-on exposure to Vertex AI, Kubeflow, ML pipelines, or equivalent ML platforms.
  • Experience deploying and scaling ML workloads on cloud-native infrastructure.
  • Knowledge of GPU-based workloads, distributed training, and ML resource scheduling is highly desirable.
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