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# Machine Learning Engineer

- **Company:** The Local Europe AB
- **Location:** Nashville, TN, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence, Amazon Web Services, Application Integration Architecture, Cloud Computing, Data Infrastructure, Data Stores, DevOps, Monitoring of Systems, Python (Programming Language), Machine Learning, Tensorflow, Search Technologies, SQL Databases, Text Mining, Unstructured Data, Pytorch, Fast Healthcare Interoperability Resources, Large Language Models, Electronic Medical Records, Generative AI, Git, Pandas, Build Management, Scikit Learn, Health Level Seven International, Machine Learning Operations
- **Published:** June 14, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=e0485665283ed362

## About the Role

Do you have experience in Text mining tools?, * 6+ years in ML engineering, data science, or applied AI roles (bonus for healthcare experience).

 * Proficient in Python (Pandas, Scikit-learn, LangChain, PyTorch or TensorFlow) and SQL.
 * Strong experience building and deploying models in production environments.
 * Familiar with LLM integration, retrieval-augmented generation (RAG), and vector search (e.g., Pinecone, FAISS).
 * Skilled in working with unstructured data: OCR, NLP, form/document understanding.
 * Experience with cloud infrastructure (AWS preferred), Git, and basic DevOps practices.
 * Comfortable partnering with product and operations teams to solve real-world business problems.
 * Working with healthcare data (EMR/EHR, insurance claims, HL7/FHIR).
 * Building patient or payer-facing AI solutions in regulated environments (HIPAA).

## Description

Local Infusion is building a next-generation operating system for infusion care - powered by automation, AI, and real-time insights. We're looking for a Machine Learning Engineer to own the intelligence layer of our platform. You'll work across structured and unstructured data to drive automation, predict bottlenecks, and help patients start treatment faster., * Model Development: Build and deploy ML models to identify missing data, predict treatment delays, triage high-risk referrals, and score payer/patient friction

 * AI Integration: Architect and maintain LLM-powered workflows
 * Data Infrastructure: Build and maintain data pipelines to collect, clean, label, and store data across systems
 * Model Monitoring: Define success metrics, monitor model performance over time, and iterate quickly to improve accuracy and reliability. Set up training data, data labeling workflows or feedback loops for model improvement.
 * Cross-Functional Partnership: Collaborate with product, engineering, and operations to identify high-leverage automation opportunities and embed models into workflows.


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