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David Careers

Machine Learning/AI Engineer

Posted yesterday

camptekaiAkron (OH)
Data ScientistsCustom Computer Programming Services

SENIORITY

Senior

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About the role

ABOUT CAMPTEK.AICampTek.ai is an AI-powered Revenue Cycle Management (RCM) company built for healthcare providers across the nation. We put AI and intelligent automation at the center of every RCM decision — from claims processing and denial management to coding accuracy and payment integrity — so healthcare finance teams can work faster, recover more revenue, and spend less time on manual rework. By streamlining the revenue cycle for hospitals, health systems, and specialty and subspecialty practices, we help providers get paid faster, operate with confidence, and accelerate care for their patients. We're a fully remote, fast-growing team-building meaningful work at the intersection of AI and healthcare.
ROLE OVERVIEW: As a Machine Learning / AI Engineer, you will build the intelligence behind CampTek.ai's revenue cycle solutions. The role spans three areas: predictive models on claims and remittance data, generative AI and agentic features built on large language models, and the data science that turns revenue cycle data into insight for our healthcare provider clients. You'll work end to end — framing problems with product and revenue cycle experts, working with messy real-world healthcare data, building and evaluating models, and deploying them on Microsoft Azure where they run reliably, securely, and at scale. KEY RESPONSIBILITIES1.
Predictive Modeling & Data Science·:
  • Build models for denial prediction, underpayment detection, propensity to pay, and work prioritization using claims, remittance (835/837), and EHR data.·
  • Run exploratory analysis and root-cause studies that uncover revenue leakage and turn findings into client recommendations.·
  • Define evaluation metrics, run rigorous experiments, and communicate results to technical and business audiences.2.
  • Generative AI & Agents·
  • Design and build LLM-powered solutions — retrieval-augmented generation, document extraction, and agentic workflows — using Azure OpenAI and Azure AI Foundry.·
  • Process unstructured documents such as payer correspondence, EOBs, and clinical documentation with Azure AI Document Intelligence and LLMs.·
  • Build prompt, evaluation, and guardrail pipelines to measure and improve accuracy, cost, and latency.3.
  • Data & MLOps on Azure·
  • Build data pipelines and features for large, sensitive healthcare datasets using Azure Machine Learning, Azure Databricks or Fabric, and Azure SQL.·
  • Deploy, monitor, and maintain models with MLOps best practices: versioning, CI/CD, drift detection, and observability.·
  • Integrate AI capabilities with our applications and UiPath automations alongside our Full-Stack and RPA developers.4.
  • Responsible AI & Compliance·
  • Protect PHI and meet HIPAA and security requirements in every model and pipeline.·
  • Design for explainability, human-in-the-loop review, and auditability so clients can trust AI-assisted decisions.
QUALIFICATIONS: Required
  • 3+ years of experience building and deploying machine learning models in production.
  • Hands-on experience with healthcare data — claims, remittance, EHR data, or ICD-10/CPT/HCPCS coding.
  • Strong Python and SQL skills and experience with ML frameworks (scikit-learn, XGBoost/LightGBM, PyTorch, or similar).
  • Experience building applications with large language models, including RAG, prompt engineering, and evaluation.
  • Experience with Microsoft Azure AI and data services (Azure OpenAI, Azure Machine Learning, or Azure AI Document Intelligence).
  • Ability to explain technical trade-offs clearly to non-technical stakeholders; self-directed in a fully remote team.
Nice to Have:
  • Experience working with PHI in HIPAA-regulated environments.
  • Experience with agent frameworks (Semantic Kernel, LangGraph, or similar).
  • Azure AI Engineer Associate or Azure Data Scientist Associate certification.
WHAT SUCCESS LOOKS LIKE:
  • First 90 days: familiar with our data and architecture; shipping improvements to an existing model or AI feature.
  • By month six: owning at least one predictive model and one LLM feature in production, with clear evaluation metrics.
  • Within your first year: AI capabilities that measurably improve client outcomes, supported by solid MLOps on Azure.

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