Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you'd like, where you'll be supported and inspired by a collaborative community of colleagues around the world, and where you'll be able to reimagine what's possible. Join us and help the world's leading organizations unlock the value of technology and build a more sustainable, more inclusive world.
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.
Capgemini is an AI-powered global business and technology transformation partner, delivering tangible business value. We imagine the future of organizations and make it real with AI, technology and people. With our strong heritage of nearly 60 years, we are a responsible and diverse group of 420,000 team members in more than 50 countries. We deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations. The Group reported 2024 global revenues of €22.1 billion.
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