AI Engineer

Inherent Technologies; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Inherent Technologies, Inc. seeks an AI Engineer to design and deploy ML, DL, and generative AI models for healthcare-related use cases in a remote US setting. You will work with Databricks, Python, and Azure ML to build end-to-end pipelines and ensure production readiness.

You will explore LLMs, RAG, and prompt engineering, while partnering with data engineers to ensure data quality. Strong communication with nontechnical stakeholders is essential for adoption and impact.

Qualifications

• BS or MS in a quantitative field with 3–7+ years of hands-on ML/AI model development.
• Experience building production ML systems; healthcare domain a plus.
• Strong proficiency in Databricks and Python data science stack; knowledge of provider platforms a plus.

Responsibilities

• Develop ML, DL, and generative AI models for RCM use cases.
• Build end-to-end ML pipelines with feature engineering, training, tuning, validation, and monitoring.
• Research LLM techniques, embeddings, RAG, prompt engineering, and document intelligence.
• Translate product requirements into model objectives and evaluation frameworks.
• Conduct EDA to uncover patterns and insights; communicate findings to stakeholders.
• Implement MLOps practices including versioning, feature stores, drift monitoring.
• Collaborate with data engineers to ensure data quality and scalable deployment in Azure Databricks environments.
• Provide cross-functional insights and drive ML adoption across platforms.

Skills

Databricks

Python

pandas

scikit-learn

NumPy

TensorFlow

MLflow

SQL

LangChain

LangGraph

OpenAI

MCP

LLMs

Education

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

Tools

Azure Databricks

Delta Lake

Azure Machine Learning

Azure Data Lake

Azure Data Factory

Job description

Position: AI Engineer

Location: Minneapolis, MN *REMOTE *

Duration: 1 Years

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.