Google Cloud Platform MLOps Engineer

We are seeking an experienced MLOps Engineer with a strong background in cloud architecture, automation, and DevOps, with a primary focus on Google Cloud Platform (Google Cloud Platform). The ideal candidate will have multi-cloud experience (AWS, Azure, Google Cloud Platform) but must demonstrate hands-on expertise in Google Cloud Platform services, particularly in networking, IAM, folder/project hierarchy, service controls, and organization policies.

Key Responsibilities:

• Design, implement, and optimize MLOps pipelines on Google Cloud Platform for scalable and cost-effective deployments.
• Automate infrastructure provisioning and configuration using Terraform, CloudFormation, and Ansible.
• Manage containerized workloads using Google Kubernetes Engine (GKE), ensuring best practices in security, scalability, and cost optimization.
• Implement and enforce IAM policies, Azure AD/Kubernetes access controls, and organization-wide security policies on Google Cloud Platform.
• Deploy and monitor ML models and services using tools such as Prometheus, Grafana, and Looker for observability and cost optimization.
• Improve deployment automation across different environments, ensuring smooth CI/CD processes.
• Work closely with data scientists and ML engineers to operationalize machine learning models while ensuring compliance with healthcare data regulations (PHI and non-PHI handling).

Required Skills & Qualifications:

• 3+ years of hands-on experience in MLOps, Cloud, and DevOps engineering.
• Strong expertise in Google Cloud Platform (Google Cloud Platform), including networking, IAM, folder/project hierarchy, service controls, and organization policies.
• Proficiency in Terraform, CloudFormation, Python, and Ansible for infrastructure automation.
• Experience with Google Kubernetes Engine (GKE), IAM, Azure AD, and Kubernetes access control.
• Strong monitoring and logging skills using Prometheus, Grafana, and Looker.
• Experience in cost optimization and auto-scaling in cloud environments.
• Working knowledge of AWS Lambda, Cloud Run, and serverless services is a plus.
• Experience with source control tools (GitHub, GitLab, Bitbucket, etc.).
• Healthcare industry experience and familiarity with PHI handling is a plus.

Preferred Qualifications:

• Experience working in multi-cloud environments (AWS, Azure, Google Cloud Platform).
• Strong understanding of DevOps best practices and security compliance in cloud environments.
• Familiarity with ML model deployment strategies in cloud environments.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

We are seeking an experienced MLOps Engineer with a strong background in cloud architecture, automation, and DevOps, with a primary focus on Google Cloud Platform (Google Cloud Platform). The ideal candidate will have multi-cloud experience (AWS, Azure, Google Cloud Platform) but must demonstrate hands-on expertise in Google Cloud Platform services, particularly in networking, IAM, folder/project hierarchy, service controls, and organization policies.

Key Responsibilities:

• Design, implement, and optimize MLOps pipelines on Google Cloud Platform for scalable and cost-effective deployments.
• Automate infrastructure provisioning and configuration using Terraform, CloudFormation, and Ansible.
• Manage containerized workloads using Google Kubernetes Engine (GKE), ensuring best practices in security, scalability, and cost optimization.
• Implement and enforce IAM policies, Azure AD/Kubernetes access controls, and organization-wide security policies on Google Cloud Platform.
• Deploy and monitor ML models and services using tools such as Prometheus, Grafana, and Looker for observability and cost optimization.
• Improve deployment automation across different environments, ensuring smooth CI/CD processes.
• Work closely with data scientists and ML engineers to operationalize machine learning models while ensuring compliance with healthcare data regulations (PHI and non-PHI handling).

Required Skills & Qualifications:

• 3+ years of hands-on experience in MLOps, Cloud, and DevOps engineering.
• Strong expertise in Google Cloud Platform (Google Cloud Platform), including networking, IAM, folder/project hierarchy, service controls, and organization policies.
• Proficiency in Terraform, CloudFormation, Python, and Ansible for infrastructure automation.
• Experience with Google Kubernetes Engine (GKE), IAM, Azure AD, and Kubernetes access control.
• Strong monitoring and logging skills using Prometheus, Grafana, and Looker.
• Experience in cost optimization and auto-scaling in cloud environments.
• Working knowledge of AWS Lambda, Cloud Run, and serverless services is a plus.
• Experience with source control tools (GitHub, GitLab, Bitbucket, etc.).
• Healthcare industry experience and familiarity with PHI handling is a plus.

Preferred Qualifications:

• Experience working in multi-cloud environments (AWS, Azure, Google Cloud Platform).
• Strong understanding of DevOps best practices and security compliance in cloud environments.
• Familiarity with ML model deployment strategies in cloud environments.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.