AI/ML Engineer - Remote Opportunity

We are seeking an AI/ML Engineer for a fully remote, one-year engagement focused on developing machine learning, deep learning, and generative AI solutions for Revenue Cycle Management (RCM) use cases. The position requires strong hands-on experience with Databricks, Python, SQL, and modern AI technologies, including agentic AI frameworks.

You will develop production-quality AI and machine learning models, build end-to-end ML pipelines, and explore advanced techniques involving large language models, retrieval-augmented generation (RAG), embeddings, and document intelligence. Working closely with Data Engineers and cross-functional teams, you will help turn operational and product requirements into practical AI solutions that support workflow optimization and Provider Engineering platforms., * Develop machine learning, deep learning, and generative AI models for RCM use cases, including claim outcome prediction, denials classification, next-best-action recommendations, clinical appeal summarization, and workflow optimization.

• Build end-to-end machine learning pipelines covering feature engineering, model training, hyperparameter tuning, validation, and monitoring.
• Research and apply advanced AI techniques involving LLMs, embeddings, retrieval-augmented generation (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 identify data patterns, anomalies, and business insights.
• Apply MLOps best practices, including model versioning, reproducibility, feature stores, and model drift monitoring.
• Partner with Data Engineers to ensure feature availability, data quality, and scalable AI/ML deployment across Azure Databricks and enterprise platforms.
• Collaborate with cross-functional teams to communicate insights, present model results, and support the adoption of machine learning solutions.
• Stay informed about emerging AI technologies and recommend enhancements or new opportunities for Provider Engineering platforms.

• 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 machine learning or AI models in an applied industry setting.
• Strong proficiency in Databricks, Python, and SQL.
• Practical experience with Python libraries and frameworks, including pandas, scikit-learn, NumPy, TensorFlow, and MLflow.
• Strong working knowledge of GitHub Copilot or an equivalent enterprise-approved AI coding assistant.
• Experience building production-quality machine learning systems with a strong foundation in statistical modeling.
• Hands-on experience in at least one of the following areas: classification, regression, time series, recommendation systems, agentic AI, large language models (LLMs), or RAG.
• Ability to work in a fast-paced environment involving rapid prototyping, ambiguity, and iterative delivery.
• Self-motivated and solution-oriented, with the ability to independently research, troubleshoot, and resolve technical or data challenges.
• Ability to communicate technical findings to non-technical stakeholders., * Healthcare industry experience, particularly knowledge of the US healthcare system.
• Revenue Cycle Management (RCM) experience, including familiarity with EDI transactions such as 837 and 835.
• Experience with agentic AI frameworks and technologies, including LangChain, LangGraph, OpenAI, and MCP.

Relevant Tools and Technologies

• Programming and Data Science: Python, pandas, scikit-learn, NumPy, TensorFlow, SQL.
• Machine Learning and MLOps: Databricks, MLflow.
• Agentic AI and Generative AI: LangChain, LangGraph, OpenAI, MCP, LLMs, RAG, embeddings, prompt engineering.
• Cloud and Data Platforms: Azure Databricks, Azure Machine Learning, Azure Data Lake, Azure Data Factory, Delta Lake.
• Development Tools: GitHub Copilot or equivalent enterprise-approved AI coding assistant.