Cloud AI Engineer — MLOps, Terraform & GKE Expert

Jobtailor; Troy (MO); On-site; USD 120,000 - 160,000; Full time

Job summary

Jobtailor in the United States is seeking an experienced machine learning engineer to design, build, and deploy scalable ML solutions using Gemini Enterprise and Google Cloud services.

You will develop and maintain MLOps pipelines, provision cloud infrastructure with Terraform, and deploy ML workloads on Google Kubernetes Engine (GKE) with secure VPC networking, collaborating with data scientists and engineers to deliver production-ready AI solutions.

Qualifications

• Experience designing, building, and deploying scalable ML solutions on Gemini Enterprise and Google Cloud.
• Experience developing and maintaining MLOps pipelines for training, deployment, monitoring, and lifecycle management.
• Experience provisioning and managing cloud infrastructure using Terraform.
• Experience deploying ML workloads on GKE with secure VPC networking.
• Strong collaboration with data scientists and engineers to deliver production-ready AI solutions.

Responsibilities

• Design, build, and deploy scalable ML solutions using Gemini Enterprise and Google Cloud.
• Develop and maintain MLOps pipelines for training, deployment, monitoring, and lifecycle management.
• Provision and manage cloud infrastructure with Terraform ensuring automation and compliance.
• Deploy ML workloads on GKE with secure and optimized VPC networking.
• Collaborate with data scientists, engineers, and stakeholders to deliver production-ready AI solutions.

Skills

Machine Learning Engineering Expertise

Google Cloud Platform (GCP) Expertise

MLOps Pipeline Development

Terraform Infrastructure Management

Google Kubernetes Engine (GKE) Deploym

Tools

Gemini Enterprise

Google Cloud Services

Terraform

Google Kubernetes Engine (GKE)

Job description

Jobtailor in the United States is seeking an experienced machine learning engineer to design, build, and deploy scalable ML solutions using Gemini Enterprise and Google Cloud services.

You will develop and maintain MLOps pipelines, provision cloud infrastructure with Terraform, and deploy ML workloads on Google Kubernetes Engine (GKE) with secure VPC networking, collaborating with data scientists and engineers to deliver production-ready AI solutions.