AI Platform Engineer: MLOps on GCP & Kubernetes

Atos SE; Troy, Northern (MI, KY); Hybrid; USD 120,000 - 160,000; Full time

Job summary

Atos is seeking a Machine Learning Engineer to design, deploy, and manage scalable AI solutions on Gemini Enterprise and Google Cloud. You will build and maintain ML pipelines, ensure secure cloud infrastructure with Terraform, and operate ML workloads on GKE with VPC networking.

You’ll collaborate with data scientists and engineers to deliver production-ready AI systems. The role focuses on end-to-end ML development, deployment, monitoring, and governance within a modern cloud environment.

Qualifications

• Experience designing and deploying ML solutions on Gemini Enterprise and Google Cloud.
• Experience deploying ML workloads using GKE and managing VPC networking.
• Proficiency with Terraform for cloud provisioning.
• Experience building ML lifecycle pipelines (MLOps) and production deployment.

Responsibilities

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

Skills

Machine Learning Engineering

Cloud Architecture

Tools

Gemini Enterprise

Terraform

Google Kubernetes Engine

Virtual Private Cloud

MLOps

Google Cloud Platform

Job description

Atos is seeking a Machine Learning Engineer to design, deploy, and manage scalable AI solutions on Gemini Enterprise and Google Cloud. You will build and maintain ML pipelines, ensure secure cloud infrastructure with Terraform, and operate ML workloads on GKE with VPC networking.

You’ll collaborate with data scientists and engineers to deliver production-ready AI systems. The role focuses on end-to-end ML development, deployment, monitoring, and governance within a modern cloud environment.