Vacancy expired!
- Take offline models data scientists build and turn them into a real machine learning production system .
- Build and deploy training and serving pipelines for ML models in Google Cloud Platform .
- Identify and evaluate new technologies to improve performance, maintainability, and reliability of existing machine learning systems .
- Work with upstream and downstream engineering teams to define the data contract for serving in platforms such as Seldon Core, Vertex AI .
- Work as a liaison with Data Science and Data Engineering team to understand the data schema and convey changes and updates to the respective teams Qualifications .
- Experience developing with containers and Kubernetes in cloud computing environments (AWS/Google Cloud Platform) .
- Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.) .
- Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.)
- Experience working as one or more of the following DevOps Engineer, SRE, Platform Engineer, Infrastructure Engineer, Cloud Engineer, and/or Production Engineer.
- Familiarity and working knowledge with Kubeflow, Seldon Core, Artifact Registry Knowledge of Unit testing in Python, Mocking, Pytest.
- Knowledge of model serialization and deserialization for Pytorch, Scikitlearn and Tensorflow .
- Knowledge of Git and GitHub. Gitops, Bazel and CI/CD deployments with Jenkins Understanding of MLOps, Model development lifecycle with knowledge of Training and Deployment pipelines for Machine Learning solutions.
- Nice to have model monitoring and debugging experience in production .
- Nice to have experience with BigQuery.
Vacancy expired!