Role overview

Machine Learning Engineer

Requirements and responsibilities

Readable role content extracted into sections for faster review.

Machine Learning & Statistical Modeling

  • 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)

NLP & LLM Engineering

  • Tune the existing CRC NLP engine for clinical note understanding, keyword extraction, concept expansion, negation detection, and sentiment scoring
  • Build custom clinical embeddings using HuggingFace Transformers, spaCy, and domain-tuned vector models
  • 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

AI-Driven Product Development

  • Architect, build, and deploy the AI Analytics Chatbot, integrating model logic, business rules, and Fabric/Databricks data sources
  • 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

MLOps & Engineering

  • Build maintainable ML pipelines (training, validation, deployment) using Databricks, Fabric, MLflow, GitHub, and CI/CD
  • Implement model monitoring, drift detection, and automated retraining
  • Package and deploy reproducible models via APIs or scheduled Fabric/Databricks workflows

Collaboration & Delivery

  • Work with data engineering to embed models into CRC applications
  • Partner with BI analysts to transform model outputs into dashboards
  • Document methodologies, assumptions, architecture, and validation processes clearly

Required Skills & Experience

  • 3–6 years of hands-on machine learning engineering experience (not just DS notebooks)
  • Strong Python engineering background: pandas, scikit-learn, statsmodels, PyTorch or TensorFlow, transformers, spaCy
  • Experience building and tuning LLM and NLP pipelines end-to-end
  • Experience with regression, ordered probit/logit, hierarchical models, and general statistical modeling
  • Experience deploying ML workloads in Databricks, Azure ML, and Fabric
  • Strong SQL for feature engineering and model validation
  • Prior experience working with healthcare data (EMR, claims, RCM, CMS) preferred
  • Strong communication and the ability to explain complex ML systems to non-technical stakeholders.
  • Proactive, self-managing engineer who can independently own ML systems end-to-end.
  • Fluent English required

Preferred Qualifications

  • Experience with:Retrieval-Augmented Generation (RAG) pipelinesVector databases (FAISS, Chroma, Pinecone, Qdrant)Enterprise chatbot frameworksMLflow, CI/CD, GitHub Actions, and model versioningPower BI integration for ML outputsFHIR/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

Details

  • 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

Certifications considered:

  • Databricks ML Associate/Professional
  • Azure AI Engineer Associate
  • DeepLearning.AI NLP/LLM specializations
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Browse stack
FocusMachine Learning EngineeringRole area
Seniority signalSeniorCandidate level
StackAzure, CI/CD, LLMPrimary skills
Location1 accepted countryEligibility

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