AI/ML Engineer

Inherent Technologies, Inc.; Washington (District of Columbia); Remote; USD 110,000 - 170,000; Full time

Job summary

Inherent Technologies, Inc. is seeking an AI/ML Engineer to develop ML, DL, and generative AI models for RCM use cases. You will build end-to-end pipelines, apply advanced LLM techniques, and collaborate with Data Engineers to deploy on Azure Databricks and related platforms.

The role requires hands-on experience with Python ML stacks, Databricks, SQL, and agentic AI frameworks, plus a healthcare familiarity. Remote work enables rapid prototyping in a fast-paced environment.

Qualifications

• Bachelor's or Master's in Data Science, CS, Stats, or math.
• 3+ years of hands‑on ML/AI experience in an applied industry.

Responsibilities

• Develop ML, DL, and generative AI models for healthcare use cases.
• Build end-to-end ML pipelines with feature engineering, training, tuning, validation, monitoring.
• Research and apply advanced techniques in LLMs, embeddings, retrieval augmentation (RAG), prompts, and document intelligence.
• Translate requirements into model objectives, data specs, and evaluation frameworks.
• Conduct exploratory data analysis to understand data patterns and insights.
• Implement MLOps practices: versioning, reproducibility, feature stores, drift monitoring.
• Collaborate with Data Engineers to ensure data quality and deployment in Azure Databricks.
• Communicate insights and model results to cross-functional teams to drive adoption.
• Stay updated on AI tech and propose enhancements for Provider Engineering platforms.

Skills

Databricks

Python ML stack

SQL

LLM/Agentic AI

Education

Bachelor's or Master's in quantitative field

Tools

Azure Databricks

Azure Machine Learning

Azure Data Lake / Data Factory

Job description

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)

Position

AI/ML Engineer

Location

***REMOTE

Duration

1 Years

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

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 stak