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
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