Machine Learning Engineer

Role overview

Responsibilities

• check_circle Build, tune, and validate statistical models including multi-stage regression, ordered probit, and generalized linear models, audit automation, acuity scoring, and financial forecasting Engineer features from structured and unstructured healthcare data (EMR, claims, revenue cycle, clinician notes)
• check_circle Tune the existing CRC NLP engine for clinical note understanding, keyword extraction, concept expansion, negation detection, and sentiment scoring
• check_circle Build custom clinical embeddings using HuggingFace Transformers, spaCy, and domain-tuned vector models
• check_circle Develop and maintain a CRC private LLM, trained on internal knowledge bases, documentation, analytics logic, and care guidelines Build automated pipelines for LLM evaluation, retraining, retrieval-augmented generation (RAG), and grounded QA
• check_circle Architect, build, and deploy the AI Analytics Chatbot, integrating model logic, business rules, and Fabric/Databricks data sources
• check_circle Integrate ML models into production services using notebooks, APIs, or batch inference jobs Support creation of AI-generated reporting, insights summaries, and automated clinical/financial narratives
• check_circle Build maintainable ML pipelines (training, validation, deployment) using Databricks, Fabric, MLflow, GitHub, and CI/CD
• check_circle Implement model monitoring, drift detection, and automated retraining Package and deploy reproducible models via APIs or scheduled Fabric/Databricks workflows
• check_circle Work with data engineering to embed models into CRC applications
• check_circle Partner with BI analysts to transform model outputs into dashboards
• check_circle Document methodologies, assumptions, architecture, and validation processes clearly
• check_circle 3–6 years of hands-on machine learning engineering experience (not just DS notebooks)
• check_circle Strong Python engineering background: pandas, scikit-learn, statsmodels, PyTorch or TensorFlow, transformers, spaCy
• check_circle Experience building and tuning LLM and NLP pipelines end-to-end
• check_circle Experience with regression, ordered probit/logit, hierarchical models, and general statistical modeling
• check_circle Experience deploying ML workloads in Databricks, Azure ML, and Fabric
• check_circle Strong SQL for feature engineering and model validation
• check_circle Prior experience working with healthcare data (EMR, claims, RCM, CMS) preferred
• check_circle Strong communication and the ability to explain complex ML systems to non-technical stakeholders.
• check_circle Proactive, self-managing engineer who can independently own ML systems end-to-end.
• check_circle Fluent English required

Preferred qualifications

• Experience with: Retrieval-Augmented Generation (RAG) pipelines Vector databases (FAISS, Chroma, Pinecone, Qdrant) Enterprise chatbot frameworks MLflow, CI/CD, GitHub Actions, and model versioning Power BI integration for ML outputs FHIR/SMART on FHIR
• Retrieval-Augmented Generation (RAG) pipelines
• Vector databases (FAISS, Chroma, Pinecone, Qdrant)
• Enterprise chatbot frameworks
• MLflow, CI/CD, GitHub Actions, and model versioning
• Power BI integration for ML outputs
• FHIR/SMART on FHIR
• Databricks ML Associate/Professional
• Azure AI Engineer Associate
• DeepLearning.AI NLP/LLM specializations