Job Summary

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

Key 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.

Skill Requirements

• Databricks
• Python (pandas, scikit-learn, NumPy, TensorFlow, MLflow)
• SQL

• Azure Databricks (MLflow, Delta Lake)
• Azure Machine Learning
• Azure Data Lake / Azure Data Factory (in partnership with Data Engineering)

• Classification, regression, time series, or recommendation systems
• Agentic AI / Large Language Models (LLMs) / RAG

Other Requirements