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 worklo, 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 worklo.
- 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.
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.
- Extensive experience with GKE, Kubernetes, Docker, and containerized worklo.
- Proven experience managing large-scale clusters, distributed worklo, autoscaling, and resource optimization.
- Strong understanding of cluster networking, observability, high availability, and troubleshooting.
- 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 worklo on cloud-native infrastructure.
- Knowledge of GPU-based worklo, distributed training, and ML resource scheduling is highly desirable.