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Google Cloud Platform Infrastructure & Automation Engineer
Jobs via Dice, Atlanta, Georgia, United States, 30383
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We are seeking a highly skilled
Google Cloud Platform Infrastructure & Automation Engineer
with expertise in
Vertex AI
to design, implement, and manage scalable, secure, and automated cloud environments. The role involves working closely with data science, MLOps, and DevOps teams to support machine learning workflows, model deployment, and end-to-end automation on Google Cloud.
Key Responsibilities:
Design, deploy, and maintain Google Cloud Platform infrastructure for ML and AI workloads using Terraform, Deployment Manager, or similar IaC tools. Build and manage CI/CD pipelines for ML models and applications on Vertex AI. Automate environment provisioning, monitoring, and scaling for AI/ML workloads. Collaborate with Data Science teams to streamline data pipelines and model deployment processes. Implement MLOps best practices for training, testing, and deploying models at scale. Optimize Google Cloud Platform services usage for performance, cost-efficiency, and security. Monitor system health and troubleshoot performance issues in AI/ML deployments. Ensure compliance with security, governance, and reliability standards in cloud environments.
Required Skills & Experience:
5+ years in Cloud/DevOps engineering, with at least 3 years on Google Cloud Platform. Hands-on experience with Vertex AI, AI/ML pipeline orchestration, and model deployment. Strong knowledge of Google Cloud Platform services: GKE, Cloud Run, BigQuery, Cloud Storage, Pub/Sub, AI Platform, etc. Proficiency in Terraform, Ansible, or equivalent IaC tools. Experience with CI/CD tools (Cloud Build, Jenkins, GitLab CI, etc.). Familiarity with Docker, Kubernetes, and container orchestration in Google Cloud Platform. Knowledge of Python, Bash, or Go for automation scripts. Strong understanding of networking, security, and IAM in Google Cloud Platform.
Nice-to-Have:
Experience with Kubeflow, MLflow, or TFX. Understanding of data engineering workflows in Google Cloud Platform. Google Cloud Platform Professional Cloud DevOps Engineer or Professional Machine Learning Engineer certification.
Education:
Bachelor s or Master s degree in Computer Science, Engineering, or related field. Seniority level
Seniority level
Mid-Senior level Employment type
Employment type
Full-time Job function
Job function
Engineering and Information Technology Industries
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Were unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI. #J-18808-Ljbffr
We are seeking a highly skilled
Google Cloud Platform Infrastructure & Automation Engineer
with expertise in
Vertex AI
to design, implement, and manage scalable, secure, and automated cloud environments. The role involves working closely with data science, MLOps, and DevOps teams to support machine learning workflows, model deployment, and end-to-end automation on Google Cloud.
Key Responsibilities:
Design, deploy, and maintain Google Cloud Platform infrastructure for ML and AI workloads using Terraform, Deployment Manager, or similar IaC tools. Build and manage CI/CD pipelines for ML models and applications on Vertex AI. Automate environment provisioning, monitoring, and scaling for AI/ML workloads. Collaborate with Data Science teams to streamline data pipelines and model deployment processes. Implement MLOps best practices for training, testing, and deploying models at scale. Optimize Google Cloud Platform services usage for performance, cost-efficiency, and security. Monitor system health and troubleshoot performance issues in AI/ML deployments. Ensure compliance with security, governance, and reliability standards in cloud environments.
Required Skills & Experience:
5+ years in Cloud/DevOps engineering, with at least 3 years on Google Cloud Platform. Hands-on experience with Vertex AI, AI/ML pipeline orchestration, and model deployment. Strong knowledge of Google Cloud Platform services: GKE, Cloud Run, BigQuery, Cloud Storage, Pub/Sub, AI Platform, etc. Proficiency in Terraform, Ansible, or equivalent IaC tools. Experience with CI/CD tools (Cloud Build, Jenkins, GitLab CI, etc.). Familiarity with Docker, Kubernetes, and container orchestration in Google Cloud Platform. Knowledge of Python, Bash, or Go for automation scripts. Strong understanding of networking, security, and IAM in Google Cloud Platform.
Nice-to-Have:
Experience with Kubeflow, MLflow, or TFX. Understanding of data engineering workflows in Google Cloud Platform. Google Cloud Platform Professional Cloud DevOps Engineer or Professional Machine Learning Engineer certification.
Education:
Bachelor s or Master s degree in Computer Science, Engineering, or related field. Seniority level
Seniority level
Mid-Senior level Employment type
Employment type
Full-time Job function
Job function
Engineering and Information Technology Industries
Software Development Referrals increase your chances of interviewing at Jobs via Dice by 2x Sign in to set job alerts for Automation Engineer roles.
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Atlanta, GA $120,000.00-$120,000.00 2 weeks ago Atlanta, GA $110,000.00-$150,000.00 1 week ago Senior Controls Engineer - HVAC Building Automation (BAS) - Automated Logic
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Were unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI. #J-18808-Ljbffr