Lead AI/ML – Build Agentic LLM Systems Powering the Future of Healthcare

Company overview: Join a fast-growing health-tech company that's reimagining primary care for the modern world. As the Lead AI/ML Architect, you'll design and build next-gen Agentic AI systems that enable clinical teams to deliver smarter, value-based healthcare. You'll lead the architecture for multi-agent orchestration, RAG pipelines, and LLM-driven reasoning frameworks—shaping how millions experience care. The company recently raised $6 million in funding to scale its AI-first care platform, unifying providers, patients, and workflows across the U.S. healthcare system. You'll collaborate with top engineers and data scientists to turn AI innovation into real-world healthcare impact—from automating clinical tasks to enhancing care quality and outcomes. Years of experience: 8-17 Years. Responsibilities:  Lead architecture, design, and implementation of LLM-based and agentic AI systems for clinical and operational use cases.  Oversee the development of multi-agent orchestration frameworks (reasoning, planning, and task execution) using tools such as LangGraph, CrewAI, or Semantic Kernel.  Build scalable RAG pipelines and retrieval systems using vector databases (Pinecone, FAISS, Weaviate, Vertex AI Matching Engine).  Guide engineers on prompt design, model evaluation, multi-step orchestration, and hallucination control.  Collaborate with product managers, data engineers, and designers to align AI architecture with business goals.  Manage end-to-end AI lifecycle — data ingestion, fine-tuning, evaluation, deployment, and monitoring on Vertex AI / AWS Bedrock / Azure OpenAI.  Lead scrum ceremonies, sprint planning, and backlog prioritization for the AI team.  Work directly with external stakeholders and customer teams to understand requirements, gather feedback, and translate insights into scalable AI solutions.  Ensure compliance with HIPAA, PHI safety, and responsible AI governance practices.  Contribute to hiring, mentoring, and upskilling the AI engineering team. Must-Have Skills:  Deep expertise in LLMs, RAG, and Agentic AI architectures, including multi- agent planning and task orchestration. Hands-on experience with LangChain, LangGraph, CrewAI, or Semantic Kernel.  Strong proficiency in Python, cloud-native systems, and microservice-based deployments.  Proven track record of leading AI projects from concept to production, including performance optimization and monitoring.  Experience working with healthcare data models (FHIR, HL7, clinical notes) or similar regulated domains.  Experience leading agile/scrum teams, with strong sprint planning and delivery discipline.  Excellent communication and collaboration skills for customer-facing discussions, technical presentations, and cross-team coordination.  Deep understanding of prompt engineering, LLM evaluation, and hallucination mitigation. General Skills:  Strong leadership, mentorship, and people management abilities.  Excellent written and verbal communication for both technical and non-technical audiences.  Ability to balance technical depth with product priorities and delivery timelines.  Adaptability to fast-changing AI technologies and ability to evaluate new tools pragmatically.  A bias toward ownership and proactive problem-solving in ambiguous situations.  Empathy for end-users and a commitment to responsible AI in healthcare. Good to Have:  Experience leading AI platform initiatives or building internal AI tooling.  Exposure to MLOps, continuous evaluation pipelines, and observability tools for LLM systems.  Knowledge of multi-modal AI (text + structured + image data).  Prior experience integrating AI into production SaaS platforms or healthcare systems