AI / ML Architect

GE Healthcare GE Healthcare · Healthcare · Seongnam, Gyeonggi-do, South Korea +1 · Digital Technology / IT

GE Healthcare is seeking an AI/ML Architect to design, develop, and operationalize advanced data science and machine learning solutions for their product portfolio, focusing on integrating AI into healthcare products within regulated environments. The role involves leading the architecture of end-to-end ML systems, developing predictive models, collaborating with cross-functional teams, and ensuring regulatory compliance and MLOps best practices.

What you'd actually do

  1. Lead the design and architecture of end-to-end data science and machine learning solutions, including data pipelines, model development, deployment, and monitoring.
  2. Develop and validate predictive models, statistical analyses, and AI algorithms to enhance product functionality and performance.
  3. Collaborate closely with cross-functional teams (software, hardware, systems engineering) to translate business and product requirements into scalable data solutions.
  4. Design and implement robust testing and validation frameworks for machine learning models to ensure accuracy, reliability, and regulatory compliance.
  5. Lead adoption of MLOps best practices, including CI/CD for models, versioning, monitoring, and governance.

Skills

Required

  • Python
  • R
  • Pandas
  • NumPy
  • TensorFlow
  • PyTorch
  • Scikit-learn
  • Spark
  • Hadoop
  • Azure
  • AWS
  • GCP
  • C++
  • C#
  • software development lifecycle
  • model validation
  • testing practices

Nice to have

  • Master’s or PhD in Data Science, Computer Science, AI, Statistics, or related field
  • healthcare data
  • medical imaging
  • MLOps
  • model governance
  • explainable AI (XAI)
  • data privacy laws
  • HIPAA
  • GDPR
  • image processing
  • DICOM

What the JD emphasized

  • regulated environments
  • regulatory compliance

Other signals

  • design and architecture of end-to-end data science and machine learning solutions
  • Develop and validate predictive models, statistical analyses, and AI algorithms
  • integrate machine learning and data-driven capabilities into products
  • Design and implement robust testing and validation frameworks for machine learning models
  • Optimize data infrastructure and workflows
  • Lead adoption of MLOps best practices