Senior Ai/ml Engineer

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

Senior AI/ML Engineer at GE Healthcare responsible for designing, developing, and delivering advanced data science and machine learning solutions to enhance product capabilities. Focuses on hands-on model development and implementation, translating AI strategies into production-ready solutions, and integrating data-driven intelligence into products. Collaborates with cross-functional teams for scalable performance in real-world environments.

What you'd actually do

  1. Develop and implement machine learning models, statistical analyses, and AI algorithms to enhance product functionality and performance.
  2. Take ownership of end-to-end model development including data preparation, model training, evaluation, and validation.
  3. Implement scalable and efficient data pipelines to support model development and deployment.
  4. Collaborate with software engineers and system teams to integrate machine learning models into production systems.
  5. Support model deployment, monitoring, and performance optimization in production environments.

Skills

Required

  • Python
  • Pandas
  • NumPy
  • TensorFlow
  • PyTorch
  • Scikit-learn
  • data pipeline development
  • large-scale data processing
  • software development lifecycle
  • model validation
  • model testing
  • object-oriented design
  • C++
  • C#
  • Azure
  • AWS
  • GCP
  • English communication

Nice to have

  • healthcare data
  • medical imaging
  • regulated environments
  • image processing
  • CUDA
  • OpenCL
  • GPGPU programming
  • real-time software
  • optimization techniques for running AI models on GPU
  • SQL
  • MLOps
  • model governance
  • explainable AI (XAI)

What the JD emphasized

  • real-world environments
  • model development
  • model training
  • model evaluation
  • model validation
  • model deployment
  • model monitoring
  • model performance optimization
  • model accuracy
  • model robustness
  • model reliability

Other signals

  • model development
  • model implementation
  • production systems
  • model deployment
  • model monitoring
  • model performance optimization
  • model validation
  • model testing