Job Description

Duties and Responsibilities:

• MLOps engineer with 7-10 years of experience in ML model deployment for a client based in Korea. The MLOps engineer will be willing to work on location for the duration of the project (6-12 months), returning to Japan on completion.
• Design and implement CI/CD pipelines for training, testing, and deploying ML models.
• Build and maintain scalable ML infrastructure using cloud platforms AWS and some exposure to containerization (Docker, Kubernetes).
• Experience moving models from research to production, ensuring efficient, scalable, and reliable deployment.
• Continuously monitor model performance, data drift, accuracy, and resource usage in production, setting up alerts. Improve ML pipelines for efficiency, cost-effectiveness, and performance.
• Experience leading a team and/or work with data scientists (model requirements) and software engineers (integration), preferred.

Job Description - Grade Specific

Requirements and Qualifications:

Key Skills & Tools:

• Programming (Python).
• Cloud: Azure.
• Containerization (Docker, Kubernetes).
• CI/CD Tools (Jenkins, GitLab CI, Argo).
• Knowledge of / Openness to learn: Domino Datalabs for MLOps.
• Language: Korean (business level - must), Japanese (N2 or higher), English (business)

Nice to Haves:

• Experience with AWS (EKS, Sagemaker, Step Functions) and hybrid/multi-cloud patterns.
• Hands-on with model observability tools (e.g., Evidently, Prometheus/Grafana, OpenTelemetry).
• Security and compliance in ML (secrets management, IAM, encryption, audit).
• Experience with feature stores, model registries, and experiment tracking (e.g., Feast, MLflow).
• Cost optimization of training/serving workloads; GPU/accelerator-aware scheduling.
• Experience integrating with enterprise data platforms (e.g., SAP, Snowflake).

Soft Skills:

• Strong collaboration and stakeholder management across Data Science, Platform, and Application teams.
• Clear slide-making, written and verbal communication; ability to simplify complex technical topics for non-technical audiences.
• Proactive ownership, bias for automation, and continuous improvement mindset.
• Mentorship of junior engineers and championing engineering best practices.

Educational Profile:

• Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or related field; Master’s or equivalent industry experience, is a bonus.