AI Engineer (LLMs for Healthcare)
About Keebler Health
Keebler Health is building the operating system for value-based care. Our mission is to help risk-bearing healthcare organizations thrive in value-based arrangements by unlocking the full power of their data. We empower leading primary care groups, ACOs, and health plans to act on real-time insights that improve outcomes, reduce costs, and fuel sustainable growth.
We're a fast-moving, high-performing team, and we’re looking for people who share our bias toward speed, urgency, and excellence. As a member of the team, you won’t just write code or stay in your lane—you’ll shape critical systems, innovate quickly, and set a high bar for a product that supports the future of U.S. healthcare.
About the role
We are seeking a talented and motivated mid to senior level AI Engineer with expertise in developing and fine-tuning large language models (LLMs), healthcare workflows, and AI/ML engineering best practices. The ideal candidate will bring a deep understanding of healthcare-specific challenges and modern AI techniques to drive innovation in Value-Based Care solutions. Level and salary will commensurate with experience.
Key Responsibilities
AI/ML Engineering
- Fine-tune and optimize large language models (LLMs) to address specific healthcare applications.
- Develop and apply advanced prompt engineering techniques to enhance model outputs for clinical scenarios.
- Implement Retrieval-Augmented Generation (RAG) systems to improve knowledge retrieval from large datasets.
- Work with knowledge graphs to organize and integrate healthcare-specific data for enhanced decision-making.
- Evaluate black-box models using precision, recall, and other performance metrics, ensuring robustness and reliability.
Healthcare Expertise
- Collaborate with healthcare professionals to understand workflows and identify opportunities for AI-driven enhancements.
- Design and build AI models that align with healthcare standards and regulations (e.g., HIPAA compliance).
- Integrate domain-specific knowledge of healthcare data, including FHIR and interoperability standards, into AI solutions.
MLOps & Deployment
- Develop and maintain scalable, production-ready AI pipelines using MLOps tools.
- Deploy and monitor AI models in production environments to ensure performance and compliance.
- Optimize infrastructure for efficient training, testing, and deployment of models.
Innovation and Optimization
- Stay at the forefront of advancements in AI, especially in healthcare applications.
- Identify and resolve performance bottlenecks in AI workflows.
- Explore emerging trends and technologies in LLMs and healthcare to continually improve solutions.
Collaboration and Impact
- Partner with cross-functional teams, including data engineers and clinicians, to ensure seamless integration of AI into healthcare workflows.
- Communicate technical results and insights effectively to non-technical stakeholders.
Required Qualifications
- Proven experience in LLM fine-tuning and advanced prompt engineering.
- Strong background in Python and modern ML frameworks (e.g., Huggingface, pyTorch).
- Familiarity with healthcare workflows and regulatory requirements (e.g., HIPAA, FHIR standards).
- Hands-on experience with retrieval-augmented generation (RAG) techniques.
- Expertise in evaluating AI models using performance metrics like precision, and recall.
Preferred Skills
- Experience with MLOps frameworks such as MLflow, Langfuse, or similar tools.
- Understanding of healthcare data standards, including HL7 and HEDIS metrics.
- Strong problem-solving skills in integrating AI with complex healthcare datasets.
- Familiarity with cloud platforms (e.g., AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
When applying
In addition to your resume, also include:
- A highly personalized, bold, and hilarious “ Keebler Health –style” introduction that grabs attention - outgoing, fun, and uniquely you (not unique