Key Responsibilities

For the AI Engineer, required skillset are: Databricks, Python (pandas, scikit-learn, NumPy, TensorFlow, MLflow) SQL, and Agentic AI frameworks (LangChain, LangGraph, OpenAI, MCP)

Responsibilities

Develop machine learning, deep learning, and generative AI models to support RCM use cases such as claim outcome prediction, denials classification, next best action recommendation, clinical appeal summarization, and workflow optimization. Build end to end ML pipelines including feature engineering, model training, hyperparameter tuning, validation, and monitoring. Research and apply advanced techniques in LLMs, embeddings, retrieval augmentation (RAG), prompt engineering, and document intelligence. Translate operational and product requirements into measurable model objectives, data specifications, and evaluation frameworks. Conduct exploratory data analysis (EDA) to understand data patterns, anomalies, and business insights. Implement and follow best practices around MLOps, including model versioning, reproducibility, feature stores, and drift monitoring. Partner with Data Engineers to ensure feature availability, data quality, and scalable ML/AI deployment within Azure Databricks and enterprise platforms. Collaborate with cross-functional teams to communicate insights, present model results, and drive adoption of ML solutions. Maintain awareness of emerging AI technologies and propose enhancements or new opportunities for Provider Engineering platforms.

Required Qualifications

Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Applied Mathematics, or a related quantitative field. 3–7+ years of hands-on experience developing ML or AI models in an applied industry setting (healthcare experience strongly preferred). Strong proficiency in: Databricks Python (pandas, scikit-learn, NumPy, TensorFlow, MLflow) SQL

• Preferred: Knowledge of US healthcare, ideally with Revenue Cycle Management (RCM) experience, including familiarity with EDI transactions such as 837 and 835.

Ability to thrive in a fast-paced environment with rapid prototyping, ambiguity, and iterative delivery. Required: Strong working knowledge of GitHub Copilot (or equivalent enterprise-approved AI coding assistant) to accelerate development. Self-motivated and solution-oriented; able to independently research, troubleshoot, and identify paths forward when facing technical or data challenges. Experience working in Azure or similar cloud environments: Azure Databricks (MLflow, Delta Lake) Azure Machine Learning Azure Data Lake / Azure Data Factory (in partnership with Data Engineering) Hands-on experience in at least one of the following: Classification, regression, time series, or recommendation systems Agentic AI / Large Language Models (LLMs) / RAG Strong statistical modeling foundation and experience building production-quality ML systems. Ability to communicate technical findings to nontechnical stakeholders clearly and effectively