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MLOps Engineer – Tokyo, Japan

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AI Summary

Design, build, and operate ML infrastructure across AWS, GCP, or Azure. Manage containerised workloads with Kubernetes and implement CI/CD pipelines for model deployment and monitoring. Requires strong Python skills and business-level Japanese (JLPT N2 or above).

Key Highlights
MLOps Engineer role in Tokyo with visa sponsorship and relocation support
Design and operate ML infrastructure on major cloud platforms (AWS, GCP, Azure)
Develop CI/CD pipelines and automate ML workflows using Airflow, Kubeflow, or SageMaker Pipelines
Key Responsibilities
Design and operate ML infrastructure across AWS, GCP, or Azure
Build and optimise Docker images for Python applications and ML models
Manage containerised workloads using Kubernetes including EKS, GKE, or AKS
Build infrastructure through Terraform or CloudFormation
Develop CI/CD pipelines using GitHub Actions
Automate ML workflows through Airflow, Kubeflow, Vertex AI, or SageMaker Pipelines
Manage experiments and model registries with MLflow
Deploy model APIs using KServe, SageMaker Endpoints, or similar platforms
Monitor system and model performance using Prometheus, Grafana, or Datadog
Automate retraining cycles and improve infrastructure performance and cost
Technical Skills Required
Python Kubernetes CI/CD pipeline development
Benefits & Perks
Visa sponsorship available
Relocation support available
Salary from ¥7,000,000 to ¥15,000,000
Nice to Have
MLflow
Kubeflow
Airflow
SageMaker
Vertex AI
KServe
Spark
Hadoop

Job Description


MLOps Engineer – Tokyo, Japan

¥7,000,000–¥15,000,000

Permanent | Visa sponsorship and relocation support available


We’re supporting a global technology business that is expanding its AI and machine learning capability in Japan.


They are looking for an MLOps Engineer to help design, build and operate the infrastructure behind production ML systems. You’ll work alongside data scientists and AI engineers, taking models from development through to deployment, monitoring and continuous improvement.


The role


You’ll be involved in:

• Designing and operating ML infrastructure across AWS, GCP or Azure

• Building and optimising Docker images for Python applications and ML models

• Managing containerised workloads using Kubernetes, including EKS, GKE or AKS

• Building infrastructure through Terraform or CloudFormation

• Developing CI/CD pipelines using tools such as GitHub Actions

• Automating ML workflows through Airflow, Kubeflow, Vertex AI or SageMaker Pipelines

• Managing experiments and model registries with tools such as MLflow

• Deploying model APIs using KServe, SageMaker Endpoints or similar platforms

• Monitoring system and model performance using Prometheus, Grafana or Datadog

• Automating retraining cycles and improving infrastructure performance and cost


What they’re looking for

You don’t need experience with every tool listed, but you should offer strong experience across several of the following:

• Python or another relevant development language

• AWS, GCP or Azure

• Docker and Kubernetes

• Terraform or CloudFormation

• CI/CD pipeline development

• Linux-based development and operations

• Machine learning or data analysis fundamentals

• Cloud infrastructure cost optimisation


Experience with MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, KServe, Spark or Hadoop would be particularly useful.


Japanese language ability at business level is essential, ideally JLPT N2 or above.


This could suit an experienced MLOps Engineer, ML Platform Engineer, DevOps Engineer or Cloud Engineer who wants to move further into production AI and machine learning infrastructure.


The package

• Salary from ¥7,000,000 to ¥15,000,000, depending on experience

• Permanent position based in Tokyo

• Visa sponsorship available

• Relocation support available

• One-stage interview process


Applications are welcomed from candidates already based in Japan and suitably qualified international applicants interested in relocating.


To apply, send me your CV or message me directly for a confidential conversation.


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