Cloud Devops Developer Jobs

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:

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

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

Technologies/Enivornment involved :

IBM Cognos Analytics 11.1.x (Framework Manager, Report Studio and Event Studio), Tableau Desktop 2021.x, Oracle 21c.