Principal AI/ML Applied Scientist

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together. As a Principal AI/ML Applied Scientist, you will provide technical leadership in architecting, developing, evaluating and deploying advanced AI and Generative AI solutions that address complex business and healthcare challenges. You will combine deep expertise in machine learning, deep learning, Large Language Models (LLMs) and AI solution architecture with solid engineering judgment to translate emerging AI capabilities into scalable, reliable and responsible enterprise solutions. You will work across business, product, engineering, data science and architecture teams to define AI solution strategies, establish technical standards and accelerate the adoption of modern AI/ML capabilities across the organization.

Lead AI solution architecture for complex, enterprise-scale AI/ML and Generative AI solutions, translating business problems into scalable technical architectures and implementation strategies Architect and develop Generative AI and LLM-based applications, including Retrieval-Augmented Generation (RAG), agentic AI/workflows, conversational AI, information extraction, summarization, reasoning and other advanced AI capabilities Provide technical leadership across the complete AI/ML lifecycle, including problem formulation, data strategy, experimentation, model development, evaluation, deployment, monitoring and continuous improvement Design and establish robust LLM and Generative AI evaluation frameworks, including evaluation datasets, automated and human evaluation, model quality metrics, retrieval evaluation, hallucination and groundedness assessment, safety evaluations, regression testing and production monitoring Evaluate and select appropriate foundation models, LLMs, embedding models and AI platforms, balancing model quality, latency, scalability, security, reliability and cost Apply advanced machine learning and deep learning techniques across structured and unstructured data, including supervised and unsupervised learning, NLP, representation learning, transformer architectures and modern foundation-model approaches Design and optimize RAG architectures, including document processing, chunking, embeddings, vector search, retrieval/reranking strategies, prompt/context engineering and grounding techniques Drive experimentation with emerging approaches such as prompt engineering, fine-tuning, parameter-efficient fine-tuning, model adaptation, synthetic data generation and AI agents to improve solution performance Architect cloud-native AI/ML solutions using Azure, GCP and/or AWS, leveraging managed AI services, scalable compute, GPU infrastructure, containerization, APIs and modern deployment patterns Define scalable patterns for LLMOps/MLOps, including experimentation, model and prompt versioning, CI/CD, model deployment, observability, evaluation, monitoring and governance Research and evaluate emerging AI technologies, foundation models, open-source frameworks and industry trends, and translate relevant advancements into practical enterprise capabilities Establish technical standards, reusable frameworks, reference architectures and engineering best practices that improve the quality and velocity of AI development across teams Partner with senior stakeholders, product leaders, architects, engineers and data scientists to identify high-value AI opportunities and influence technology and solution roadmaps Provide