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Joyful HealthJoyful HealthNew York, NY

AI Engineer

Leads development of agentic AI platform to autonomously resolve healthcare insurance claim denials using multi-agent workflows, RAG systems, and LLMs. Requires 5+ years in production ML engineering with Python and frameworks like PyTorch.

150k – 275k/yr
Hybrid5+ YOEML Engineering

About the role

What you'll do

  • Design and build the architecture for our agentic AI system that autonomously resolves insurance claim denials
  • Develop specialized AI agents for denial classification, root cause analysis, evidence retrieval, policy reasoning, and appeal generation
  • Implement multi-agent orchestration frameworks that coordinate complex workflows across research, decision-making, and document generation
  • Build and optimize RAG systems to retrieve relevant clinical documentation, billing records, and payer policy information
  • Create evaluation frameworks and feedback loops to continuously improve agent performance and reliability
  • Design prompt engineering strategies and fine-tuning approaches to optimize LLM behavior for healthcare billing workflows
  • Work closely with billing managers to understand denial resolution workflows and translate them into agent behaviors
  • Collaborate with the engineering team on production infrastructure for deploying and monitoring AI agents at scale
  • Actively contribute to building the team's AI/ML vision and technical roadmap
  • Collaborate on code reviews and technical design documents to ensure code quality and distribute knowledge

We'd love to hear from you if…

  • 5+ years of experience in machine learning engineering, with a focus on building production AI systems and deploying models at scale
  • Strong experience with machine learning frameworks (PyTorch, TensorFlow, or JAX)
  • Experience working with healthcare data or understanding of medical billing workflows is a plus
  • Proficiency in:
    • Python and modern ML frameworks (PyTorch, TensorFlow, or JAX)
    • LLM technologies including prompt engineering, fine-tuning, and RAG systems
    • Vector databases and semantic search systems
    • Building reliable, production-grade AI systems with proper evaluation and monitoring
    • MLOps practices including model versioning, A/B testing, and performance tracking
    • Working with APIs and integrating AI systems into broader product workflows

Skills

PythonPyTorchTensorFlowJAXLLMsPrompt EngineeringRAGMLOpsVector DatabasesAPIs

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