AI Engineer (LLMs for Healthcare)

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.

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.

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.

• 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.

• 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.

• 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).

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 uniquely ChatGPT). Think: confident, high-energy, slightly irreverent (but still professional), with a smart nod to healthcare, value-based care, and the fact that we’re building something real.