Cloud DevOps Developer with Bachelor’s Degree in Computer Science, Computer Information Systems, Information Technology, or a combination of education and experience equating to the U.S. equivalent of a Bachelor’s degree in one of the aforementioned subjects.

Job Duties and Responsibilities:

Implement and manage version control, continuous integration, and continuous deployment (CI/CD) systems for Machine Learning models and related software components.
Monitor the performance and health of deployed Machine Learning models, making necessary adjustments and improvements to ensure optimal performance and reliability.
Develop and maintain tools and processes to automate and streamline Machine Learning model deployment, monitoring, and management.
Troubleshoot and resolve issues related to Machine Learning model deployment and infrastructure.
Document and communicate MLOps processes, guidelines and procedures to ensure consistency and knowledge sharing across the organization.
Writing production level test driven code in Python and/or Java and Scala.
Deploying and optimizing different pipelines that support various Data Science processes.
Establish and set up model Life Cycle Management with tools like MLflow, etc.
Developing and deploying Spark / Databricks jobs with enterprise tool stack including Jenkins, GitHub actions.
Deployment utilizing containerization solutions like Docker and Kubernetes.
Work as an Agile team member in a scrum team and collaborate with other Engineers and Scientists.
Work with Product Managers and Senior Scientists to deliver on the modeling needs.

Technologies Involved / Skills required for the position:

Cloud Platform: Google Cloud Platform (GCP) services, including AI Platform, Vertex AI, Dataflow,
BigQuery, Cloud Storage, and Kubernetes Engine.
Machine Learning Frameworks: TensorFlow, PyTorch, Scikit-learn.
Data Processing and Pipeline: Apache Beam - Dataflow, and Apache Airflow, DataProc.
CI/CD and Version Control: Jenkins and Git.
Containerization and Orchestration: Docker for containerization and Kubernetes with Helm.
Experiment Tracking and Model Versioning: MLflow, DVC, or TFX for tracking experiments, managing
model versions, and ensuring reproducibility.
Monitoring and Logging: Vertex AI Monitoring.
Programming Languages: Python, Scala.

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Cloud DevOps Developer