AI Engineer

GE Healthcare GE Healthcare · Healthcare · Bellevue, WA +2 · Digital Technology / IT

Seeking a Senior Staff AI Engineer to design, build, deploy, and optimize production-grade AI solutions using LLMs and foundation models for clinical applications in healthcare. The role involves developing scalable AI systems, automating clinical workflows, and ensuring responsible AI deployment, with a focus on inference pipelines, model serving, and optimization techniques.

What you'd actually do

  1. Design, develop, and deploy production-ready AI solutions using Large Language Models (LLMs), foundation models, and modern machine learning techniques to automate clinical workflows.
  2. Build scalable AI applications leveraging electronic medical records (EMRs), medical waveforms, clinical reports, and other healthcare datasets.
  3. Develop robust inference pipelines, model serving infrastructure, and AI services optimized for reliability, scalability, latency, and cost.
  4. Optimize foundation models through prompt engineering, fine-tuning, distillation, quantization, and inference optimization techniques.
  5. Implement responsible AI practices, including model evaluation, robustness testing, monitoring, and human-in-the-loop feedback mechanisms.

Skills

Required

  • Master's degree in Science, Technology, Engineering, Mathematics (STEM), Computer Science, Artificial Intelligence, or a related technical field with 3+ years of relevant experience, or PhD in a STEM discipline with relevant experience developing production AI systems.
  • Demonstrated experience building and deploying large-scale Generative AI or foundation model solutions.
  • Experience developing applications using Large Language Models (LLMs), Agentic AI, or self-supervised learning techniques.
  • Strong understanding of modern machine learning techniques including transfer learning, generative models, optimization, and model evaluation.
  • Strong programming skills in Python and C++.
  • Experience developing scalable, maintainable, production-quality software.
  • Experience designing APIs, distributed services, or cloud-native AI applications.
  • Experience with modern AI frameworks such as PyTorch, Hugging Face, DeepSpeed, Megatron, or PyTorch Lightning.
  • Experience deploying AI workloads using MLOps, ModelOps, or Foundation Model Operations (FMOps) practices.
  • Experience working with large-scale model training or inference infrastructure.
  • Experience working with high-dimensional medical imaging, waveform, or time-series clinical datasets.

Nice to have

  • Experience solving complex engineering problems with ambiguous requirements.
  • Experience deploying large-scale distributed AI systems.
  • Experience with Spark, Hadoop, TensorFlow, or PyTorch in enterprise environments.
  • Experience building production data platforms and AI-powered software applications.
  • Track record of delivering machine learning solutions using large real-world healthcare datasets.
  • Experience optimizing large-scale AI training and inference performance.

What the JD emphasized

  • production-grade AI solutions
  • large language models
  • foundation models
  • clinical applications
  • automate clinical workflows
  • responsible AI deployment
  • production AI systems
  • foundation model operations
  • large-scale model training or inference infrastructure

Other signals

  • production-grade AI solutions
  • large language models
  • foundation models
  • clinical applications
  • automate clinical workflows
  • responsible AI deployment