MLOps Engineer (Cloud & Kubernetes) - Tokyo, Japan

pt talenta eksekutif asia • Japan

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AI Summary

Design, build, and manage production-grade machine learning infrastructure on AWS, Azure, or GCP. Develop and optimize containerized AI applications using Docker and Kubernetes, and automate ML workflows with CI/CD and MLOps tools. Requires 3+ years of experience, strong Python skills, and business-level Japanese (JLPT N2 or above).

Key Highlights

On-site role in Tokyo, Japan

Design and manage cloud-based ML infrastructure

Build and optimize containerized AI applications with Docker and Kubernetes

Automate ML workflows and CI/CD pipelines

Business-level Japanese (JLPT N2) mandatory

Key Responsibilities

Design, build, and maintain cloud-based machine learning infrastructure using AWS, Microsoft Azure, or Google Cloud Platform (GCP).

Develop and optimize containerized AI applications using Docker and Kubernetes (EKS, AKS, or GKE).

Build and manage Infrastructure as Code (IaC) using Terraform or CloudFormation.

Design and maintain CI/CD pipelines for machine learning applications using GitHub Actions or similar tools.

Develop and automate machine learning workflows using Airflow, Kubeflow, MLflow, Vertex AI Pipelines, SageMaker Pipelines, or equivalent platforms.

Deploy machine learning models into production using scalable serving frameworks such as KServe or SageMaker Endpoints.

Monitor infrastructure and model performance using Prometheus, Grafana, Datadog, and other observability tools.

Optimize cloud infrastructure performance, resource utilization, operational costs, and automate model retraining processes.

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Technical Skills Required

Python Kubernetes Cloud Computing

Benefits & Perks

Visa sponsorship

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Nice to Have

Familiarity with MLflow, Kubeflow, Airflow, Vertex AI, SageMaker, Prometheus, Grafana, or Datadog

Experience with Linux system administration

Cloud cost optimization experience

Professional English communication skills

Job Description

Our client is a global technology solutions provider specializing in Artificial Intelligence (AI), Cloud Computing, Data Engineering, and Digital Transformation (DX). The organization partners with enterprise clients across multiple industries to design, deploy, and operate scalable AI platforms that support machine learning, advanced analytics, and business automation.As enterprise adoption of AI continues to accelerate, the company is expanding its MLOps Engineering team to build production-grade machine learning infrastructure, automate AI deployment pipelines, and optimize cloud-native AI platforms using modern DevOps and MLOps technologies.

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