GenAI Engineer – Healthcare Payor Data & Python Expert

Job Title: GenAI Engineer – Healthcare Payor Data & Python Expert

We are seeking a highly skilled GenAI Engineer with deep expertise in healthcare payor data and advanced Python programming skills. The ideal candidate will play a critical role in designing, developing, and deploying generative  AI solutions that drive innovation and efficiency across healthcare claims processing, payment integrity, provider reimbursement, and member engagement. This position requires a strong understanding of healthcare payor systems, including claims adjudication, CMS regulations, risk adjustment, and data interoperability. The ideal candidate will leverage AI/ML models to extract insights, automate workflows, and optimize decision-making in payer environments.

Key Responsibilities

AI Model Development & Deployment
Design, build, and fine-tune Generative AI (GenAI) models for healthcare payor use cases.
Develop, test, and optimize LLM (Large Language Models) applications tailored to payer-specific needs (e.g., automated claim audits, NLP-driven insights, pre-authorization automation).
Implement vector databases, embeddings, and retrieval-augmented generation (RAG) architectures for healthcare analytics.
Data Engineering & Integration
Work with claims, provider, member, and eligibility data to develop AI-driven automation.
Build and optimize ETL pipelines for processing structured and unstructured healthcare data.
Ensure compliance with HIPAA, HITRUST, and FHIR standards when handling sensitive healthcare data.
Python & ML Development
Develop scalable AI models using Python, TensorFlow, PyTorch, LangChain, Hugging Face, and
OpenAI APIs.
Optimize large-scale data processing using Pandas, NumPy, Dask, and Spark.
Leverage API-based architectures and deploy AI solutions via FastAPI, Flask, or Django.
Collaboration & Thought Leadership
Partner with health plan stakeholders, data scientists, and engineers to define AI-driven strategies.
Stay ahead of AI advancements in LLMs, transformer models, and healthcare-specific AI use cases.
Advocate for best practices in model interpretability, bias mitigation, and responsible AI in healthcare payor applications.