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