Principal Engineer
Principal Engineer 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:
Collaborate with data scientists, engineers, and stakeholders to develop, deploy, and maintain machine learning models in a production environment.
Design, build, and maintain scalable and robust data pipelines to support machine learning workflows.
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.
Ensure data privacy, security, and compliance with relevant regulations and best practices.
Troubleshoot and resolve issues related to machine learning model deployment and infrastructure.
Stay up-to-date on industry trends, emerging technologies, and best practices in MLOps, and contribute to the continuous improvement of the team's processes and tools.
Provide technical guidance and support to data scientists and other team members on best practices for model deployment, monitoring, and management.
Document and communicate MLOps processes, guidelines, and procedures to ensure consistency and knowledge sharing across the organization.
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.
Work location is Portland, ME with required travel to client locations throughout USA.
Rite Pros is an equal opportunity employer (EOE).
Please Mail Resumes to:
Rite Pros, Inc.
565 Congress St, Suite # 305
Portland, ME 04101.
Email: resumes@ritepros.com