■ Your Role and Responsibilities
- Research and implement various ML tools and frameworks
- Development and operation of all ML infrastructure
- Shorten development cycles for our ML based products and projects:
- Build and automate our ML workstreams from data analysis, experimentation, operationalization, model training, model tuning to visualization
- Create, improve and maintain our automated CI/CD pipelines for ML Increase our deployment velocity, including the process for deploying models and data pipelines into production
- Create, build and maintain data pipelines for analytics, model evaluation and training (includes versioning, compliance and validation). Conduct internal training and presentations about MLOps, tools benefits and usage
■ Work Location
■ Experience and Qualifications
- Development experience with Python or another high level programming language
- Experience with CI/CD tooling
- Experience with at least one ML framework (PyTorch, Tensorflow, Keras, etc.)
- Experience with at least one container technology stack (Kuberentes, Docker, etc.)
- Good understanding of ML concepts
- Strong interpersonal skills; able to work independently as well as in a team
- Experience or knowledge of at least one cloud platform (AWS, Azure, GCP, etc)
■ Additional Preferred Qualifications
- Experience with data validation and versioning
- Experience working with data scientists and/or ML engineers and building auto-scaling ML systems
- Experience in operationalization of ML projects using at least one of the popular frameworks or platforms (e.g. Kubeflow, AWS Sagemaker, Google AI Platform, Azure Machine Learning, DataRobot, DKube, etc.).
- Strong knowledge of ML models (linear regression, ensemble methods, boosting, RNN, CNN, GCN, GAN, etc.)
- Good communication and presentation skills (English or Japanese)