MLOPS Engineer

■ 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)