Overview

Join us to start Caring. Connecting. Growing together.

Responsibilities

• 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 technical mentorship and guidance to AI/ML scientists and engineers, conduct architecture and design reviews, and raise engineering and applied-science standards across teams
• Communicate complex AI concepts, architectural decisions, experimental findings and trade-offs effectively to both technical and non-technical stakeholders
• Ensure AI solutions meet enterprise expectations for responsible AI, security, privacy, reliability, explainability and governance, particularly when applied to sensitive healthcare use cases

Qualifications

• 14+ years of experience in AI/ML, Data Science, or Software Engineering, including 5+ years in AI solution architecture and technical leadership
• Hands-on experience building and deploying Generative AI, LLM, RAG, Agentic AI, and NLP-based solutions at enterprise scale
• Solid expertise in machine learning, deep learning, transformer models, prompt engineering, fine-tuning, and AI evaluation frameworks
• Proven ability to lead cross-functional teams, influence stakeholders, and deliver secure, scalable, and responsible AI solutions in enterprise environments