Keebler Health is a venture-backed healthcare technology startup committed to advancing Value-Based Care (VBC). We empower providers with AI-native solutions to streamline VBC workflows, drive clinical efficiency, and improve outcomes for patients. Our products are built to enhance the productivity of healthcare teams, enabling them to focus on what matters most—quality patient care.
Summary
We are seeking a talented and motivated 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.
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 Kubeflow, MLflow, 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).
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.