- Design, develop, and deploy LLM-based applications for clinical and operational workflows (summarization, chat, reasoning, data extraction).
- Implement and optimize RAG (Retrieval-Augmented Generation) pipelines with vector databases (FAISS, Pinecone, or Vertex AI Matching Engine).
- Work hands-on with LLM APIs (OpenAI, Claude, Gemini, Mistral, Llama) for fine-tuning, evaluation, and productionization.
- Build and maintain AI microservices / APIs using FastAPI or Flask, integrated with Fold's Unity platform.
- Implement Agentic AI workflows (multi-step reasoning, tool use, and task orchestration) using frameworks such as LangGraph, CrewAI, or Semantic Kernel.
- Collaborate with the Data Engineering and Product teams to ensure seamless integration of models with curated EHR and FHIR data.
- Conduct model evaluation and benchmarking (factual accuracy, hallucination control, latency).
Follow HIPAA-compliant development practices and ensure PHI safety and responsible AI usage
Requirements
Must have:
Knowledge of Agentic AI use cases autonomous task handling, reasoning chains, planning-execution loops.
Understanding of healthcare data formats (FHIR, HL7, clinical notes) is highly preferred.
Good to Have
Knowledge of MLOps, continuous evaluation pipelines, or model observability.
Experience integrating AI features into SaaS or API-first products.
Prior exposure to healthcare AI, clinical NLP, or population health analytics
