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.
About the job
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
Python, PyTorch, TensorFlow, JAX, LLMs, Prompt Engineering, RAG, MLOps, Vector Databases, APIs
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