Remote AI Engineer: LLMs, Databricks & MLOps

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

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.