AI Engineer

Role overview

Responsibilities

• check_circle Design, develop, and deploy production-ready AI solutions using Large Language Models (LLMs), foundation models, and modern machine learning techniques to automate clinical workflows.
• check_circle Build scalable AI applications leveraging electronic medical records (EMRs), medical waveforms, clinical reports, and other healthcare datasets.
• check_circle Develop robust inference pipelines, model serving infrastructure, and AI services optimized for reliability, scalability, latency, and cost.
• check_circle Optimize foundation models through prompt engineering, fine-tuning, distillation, quantization, and inference optimization techniques.
• check_circle Implement responsible AI practices, including model evaluation, robustness testing, monitoring, and human-in-the-loop feedback mechanisms.
• check_circle Collaborate with research scientists and cross-functional engineering teams to transition advanced AI models into production environments.
• check_circle Build reusable software components, APIs, and development frameworks that enable scalable AI application development.
• check_circle Stay current with emerging AI technologies, open-source frameworks, and industry best practices to continuously improve GE HealthCare's AI platform.

Basic qualifications

• 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.

Preferred qualifications

• 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.