Job Description Summary We are looking for an exceptional Sr Staff AI Scientist with a strong research background and deep expertise in Machine Learning, Deep Learning, NLP, Generative AI, LLMs, and Agentic AI. This role is ideal for a highly analytical and innovation-driven professional who can lead advanced AI research, design production-grade intelligent systems, and translate emerging AI capabilities into real business impact.
The ideal candidate will hold a PhD or Masters in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Computational Linguistics, Applied Mathematics, Statistics, or a related field, with proven experience in both scientific research and practical AI solution development. The candidate should also have hands-on expertise with AWS Bedrock, AWS SageMaker, and Responsible AI practices, including fairness, explainability, governance, privacy, and bias mitigation.
This role requires a rare blend of scientific depth, engineering strength, business understanding, and the ability to work across highly ambiguous and fast-evolving AI problem spaces. GE Healthcare is a leading global medical technology and digital solutions innovator. Our mission is to improve lives in the moments that matter. Unlock your ambition, turn ideas into world-changing realities, and join an organization where every voice makes a difference, and every difference builds a healthier world.
Job Description Key Responsibilities: Conduct advanced research in artificial intelligence , with focus areas including machine learning, deep learning, generative AI, large language models, natural language processing, multimodal AI, and agentic AI systems . Design, prototype, and validate novel AI algorithms, architectures, and workflows for real-world use cases. Explore and apply cutting-edge approaches in transformers, fine-tuning, retrieval-augmented generation (RAG), prompt optimization, autonomous agents, multi-agent systems, model alignment, and reasoning frameworks .
Lead experimentation across model training, evaluation, benchmarking, and optimization. Stay current with emerging AI advances and translate academic research and industry innovation into scalable enterprise solutions. Publish research findings, contribute to patents, or create internal technical thought leadership that advances the organization’s AI maturity. Build, fine-tune, and optimize ML/DL models , including supervised, unsupervised, reinforcement, and self-supervised learning systems.
Develop and deploy LLM-powered applications , conversational AI, summarization systems, semantic search, knowledge assistants, and intelligent automation platforms. Create Generative AI applications using foundation models for text, image, code, synthetic data, and multimodal outputs. Develop Agentic AI systems capable of task planning, tool usage, workflow orchestration, memory integration, retrieval, and decision support.
Use AWS Bedrock to build and scale foundation model applications, including model access, orchestration, secure integration, and GenAI experimentation. Use AWS SageMaker for model training, tuning, experimentation, MLOps, deployment, and monitoring at scale. Work with structured and unstructured data across large-scale datasets to support AI research and production systems. Lead or collaborate on data cleaning, feature engineering, data quality improvement, dataset curation, and annotation strategies .
Build robust AI pipelines that integrate with enterprise data systems, APIs, cloud services, and downstream applications. Apply SQL, NoSQL, database modeling, and data warehousing concepts to support efficient model training and inference. Partner with engineering teams to productionize models with scalability, observability, reliability, and security in mind. Ensure all AI systems are designed and deployed with strong Responsible AI principles.
Develop practices for fairness, transparency, interpretability, expla