at GE HealthCare
Full Timetex
Responsibilities:
Model Deployment and Integration: Collaborate with AI 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 AI scientists and business stakeholders. Communicate technical concepts and solutions to non-technical audiences.
Qualifications:
• Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or related field.
• Strong programming skills in languages such as Python.
• Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Knowledge of Image processing libraries in Python such a sitk and openCV.
• Knowledge of containerization and orchestration technologies (e.g., Docker).
• Familiarity with DevOps practices and tools (e.g., Git).
• Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack).
• Familiarity with software engineering principles and best practices (e.g., version control, testing, debugging).
• Strong problem-solving skills and attention to detail.
• Excellent communication and collaboration skills.
• Ability to work effectively in a fast-paced and dynamic environment.
Preferred Qualifications:
• Understanding of machine learning concepts and algorithms.
• Understanding of Image and Signal processing concepts and algorithms.
• Understanding of Large Language Models (LLM) and Foundation Models (FM).
• Certification in machine learning or related fields.
Conclusion: Joining our team as an Senior AI Engineer offers an exciting opportunity to work at the intersection of AI science, software engineering, and operations, contributing to the development and deployment of cutting-edge machine learning solutions. If you are passionate about leveraging technology to drive business value and thrive in a collaborative and innovative environment, we encourage you to apply.
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