Senior ML/LLM Engineer

2d2 days ago

U.S. Renal Care

US · Full-time

About this role

US Renal Care is expanding its Clinical Intelligence and Workflow Automation capabilities to support clinicians, reduce administrative burden, and improve patient outcomes. We are seeking a Senior ML/LLM Engineer to design and implement advanced AI systems that power intelligent decision support, workflow automation, and real-time clinical insights.

This role focuses on building agentic AI systems, Retrieval-Augmented Generation pipelines, and scalable ML infrastructure that integrates seamlessly with EHR systems, clinical workflows, and operational platforms across the organization.

The engineer will collaborate with clinicians, product leaders, and engineering teams to deliver safe, reliable, and high-impact AI solutions while ensuring compliance with HIPAA and data privacy requirements.

Opportunities include researching emerging AI technologies to improve clinician productivity and patient care, mentoring other engineers in AI-LLM/ML best practices, and participating in code reviews to maintain consistent engineering standards.

Requirements

Bachelor's degree preferred
5 years of professional experience in ML/LLM engineering, including production-grade systems
Deep experience with LLM orchestration, tool-calling, and long-context document handling
Strong proficiency with vector databases (Pinecone, Milvus, Weaviate)
Experience with MLOps: Docker, Kubernetes, CI/CD, model deployment
Proficiency with C#/.NET for backend service development
Understanding of HIPAA, PHI handling, and clinical data privacy a plus

Responsibilities

Design and implement AI Agents using frameworks such as LangGraph, Semantic Kernel, or custom orchestration layers.
Build and maintain RAG pipelines grounded in clinical guidelines, evidence-based medicine, and real-time patient data.
Develop systems that synthesize fragmented clinical data into prioritized insights for clinicians.
Architect scalable inference services capable of low-latency performance in clinical environments.
Create evaluation frameworks to ensure AI outputs are clinically accurate, unbiased, and aligned with source-of-truth medical records.
Implement vector search infrastructure using Pinecone, Milvus, or equivalent technologies.
Perform fine-tuning, PEFT/LoRA training, and model optimization for production workloads.
Build high-performance backend services using C#/.NET to integrate AI systems with existing clinical platforms.