AI Engineer

Inherent Technologies, Inc.; Minneapolis (MN); Remote; USD 120,000 - 160,000; Full time

Job summary

Inherent Technologies, Inc. is seeking an AI Engineer to design and deploy ML, DL, and generative AI models in a healthcare context. You will build end-to-end pipelines, perform EDA, and drive MLOps practices across Azure Databricks and related Azure services.

You will collaborate with cross-functional teams to translate requirements into measurable objectives, apply RAG and prompt engineering, and stay ahead of AI trends to improve Provider Engineering platforms.

Qualifications

• Bachelor's or Master's degree in a quantitative field.
• 3–7+ years of hands-on ML/AI model development experience.

Responsibilities

• Develop ML, DL, and generative AI models for healthcare use cases.
• Build end-to-end ML pipelines with feature engineering and monitoring.
• Research LLMs, embeddings, RAG, and prompt engineering.
• Translate requirements into measurable model objectives and data specs.
• Conduct EDA to uncover patterns and insights.
• Implement MLOps practices for versioning and drift monitoring.
• Collaborate with Data Engineers for feature availability and deployment on Azure Databricks.
• Communicate results to cross-functional teams and drive adoption.
• Stay updated on emerging AI tech and propose platform enhancements.

Skills

Databricks

Python

ML/AI development

AGI frameworks

Education

Bachelor's or Master's degree in Data Science/CS/Statistics

Tools

pandas

scikit-learn

NumPy

TensorFlow

MLflow

LangChain

LangGraph

OpenAI

MCP

Azure Databricks

Azure Machine Learning

Azure Data Lake

Azure Data Factory

Job description

Position: AI Engineer

Location: Minneapolis, MN***REMOTE***

Duration: 1 Year

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.