Job Description

Summary As a Staff Machine Learning Engineer, you will play a crucial role in bridging the gap between data science and production, ensuring the seamless integration and deployment of machine learning models into operational systems. You will be responsible for designing, implementing, and managing the infrastructure and workflows necessary to deploy, monitor, and maintain machine learning models at scale. GE HealthCare is a leading global medical technology and digital solutions innovator. Our purpose is to create a world where healthcare has no limits. Unlock your ambition, turn ideas into world-changing realities, and join an organization where every voice makes a difference, and every difference builds a healthier world. Responsibilities: Model Deployment and Integration: Collaborate with data scientists to optimize, package and deploy machine learning models into production environments efficiently and reliably. Infrastructure Design and Maintenance: Design, build, and maintain scalable and robust infrastructure for model deployment, monitoring, and management. This includes containerization, orchestration, and automation of deployment pipelines. Continuous Integration/Continuous Deployment (CI/CD): Implement and manage CI/CD pipelines for automated model training, testing, and deployment. Model Monitoring and Performance Optimization: Develop monitoring and alerting systems to track the performance of deployed models and identify anomalies or degradation in real-time. Implement strategies for model retraining and optimization. Data Management and Version Control: Establish processes and tools for managing data pipelines, versioning datasets, and tracking changes in model configurations and dependencies. Security and Compliance: Ensure the security and compliance of deployed models and associated data. Implement best practices for data privacy, access control, and regulatory compliance. Documentation and Knowledge Sharing: Document deployment processes, infrastructure configurations, and best practices. Provide guidance and support to other team members on MLOps practices and tools. Collaboration and Communication: Collaborate effectively with cross-functional teams, including data scientists and business stakeholders. Communicate technical concepts and solutions to non-technical audiences. Qualifications: Bachelor