AI Engineer

Noblesoft Technologies; Minnesota; Remote; USD 120,000 - 180,000; Full time

Job summary

Noblesoft Technologies Inc. is seeking an AI Engineer to develop ML, deep learning, and generative AI models supporting RCM use cases. Build end-to-end ML pipelines with feature engineering, training, tuning, validation, and monitoring in Azure Databricks and Azure ML.

The role requires strong Python and Databricks expertise, SQL proficiency, and hands-on knowledge of LangChain, LangGraph, OpenAI, and MCP within enterprise environments. Remote work is offered.

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: 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.

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.

Skills

Databricks

Python

pandas

scikit-learn

NumPy

TensorFlow

MLflow

SQL

LangChain

LangGraph

OpenAI

MCP

GitHub Copilot

Education

Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Applied Mathematics, or a related quantitative field

Tools

Azure Databricks

Azure Machine Learning

Azure Data Lake

Azure Data Factory

GitHub Copilot

Job description

Job Role - AI Engineer

Location - Remote

Key Responsibilities

Databricks, Python (pandas, scikit-learn, NumPy, TensorFlow, MLflow) SQL, and Agentic AI frameworks (LangChain, LangGraph, OpenAI, MCP)

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.