Healthcare AI Engineer: LLMs for Value-Based Care

keeblerhealth; Durham (NC); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Competitive salary and benefits

Fast-paced innovative environment

Professional growth opportunities

Collaborative team culture

Meaningful impact on healthcare

Job summary

Keebler Health is seeking a mid to senior AI Engineer to fine-tune LLMs, develop prompt strategies, and deploy RAG-based healthcare solutions. You will collaborate with clinicians and engineers to translate domain knowledge into scalable AI workflows, with emphasis on HIPAA-compliant data handling.

You will build production-grade ML pipelines, monitor models in deployment, and drive continuous improvement using modern MLOps practices and cloud infrastructure.

Qualifications

• Proven experience in LLM fine-tuning and advanced prompt engineering.
• Strong Python and ML framework experience (HuggingFace, PyTorch).
• Familiarity with healthcare workflows and regulatory requirements (HIPAA, FHIR).
• Hands-on experience with Retrieval-Augmented Generation (RAG).
• Ability to evaluate AI models using precision, recall, and other metrics.

Responsibilities

• Fine-tune and optimize large language models for healthcare applications.
• Develop and apply advanced prompt engineering techniques for clinical scenarios.
• Implement RAG systems to improve knowledge retrieval from large datasets.
• Work with knowledge graphs to organize healthcare data for decision-making.
• Evaluate models using metrics to ensure robustness and reliability.

Skills

LLM fine-tuning

Prompt engineering

Python

HuggingFace

PyTorch

RAG techniques

HIPAA/FHIR familiarity

Performance metrics (precision/recall)

Tools

Hugging Face transformers

PyTorch

Docker

Kubernetes

MLflow

Langfuse

Job description

Keebler Health is seeking a mid to senior AI Engineer to fine-tune LLMs, develop prompt strategies, and deploy RAG-based healthcare solutions. You will collaborate with clinicians and engineers to translate domain knowledge into scalable AI workflows, with emphasis on HIPAA-compliant data handling.

You will build production-grade ML pipelines, monitor models in deployment, and drive continuous improvement using modern MLOps practices and cloud infrastructure.