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.
• Implement test setup automation in cloud platforms using automation tools like Ansible.
• 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. Microsoft Azure Cloud Computing Services including AKS, Azure Databricks, MLOps, Azure Pipelines.
• 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.
• Automation: Ansible.
• 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, Fluentd, Splunk.
• Programming Languages: Python, Java, Scala.