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AI Engineer (LLMs for Healthcare)

Role overview

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.

Responsibilities

  • 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.
  • Collaborate with healthcare professionals to understand workflows and identify opportunities for AI-driven enhancements.

About the company

Keebler Health logo

Keebler Health

Keebler Health is an AI-first risk adjustment platform for providers and payers that are at risk for Medicare & Medicaid lives. Through a custom-built, transformer AI model, Keebler Health quickly uncovers risk adjustment opportunities with minimal encounter data.

Company details

Company size2 - 10

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Job description

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 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.

What We Offer

  • Competitive salary and benefits package.
  • Opportunity to work in a fast-paced, innovative environment.
  • Professional growth and development opportunities.
  • Collaborative and supportive team culture.
  • Chance to make a meaningful impact on the healthcare industry.

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MR

Marcus Rivera

Chief Revenue Officer

m.rivera@company.com
linkedin.com/in/marcusrivera
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