

Genzeon
AI Product Builder | Healthcare AI Platform
β - Featured Role | Apply direct with Data Freelance Hub
This role is for an AI Product Builder in a Healthcare AI Platform, offering a remote position for over 6 months at a pay rate of "150K/year." Requires experience in AI projects, healthcare data familiarity, and skills in RAG, document AI, and systems thinking.
π - Country
United States
π± - Currency
$ USD
-
π° - Day rate
Unknown
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ποΈ - Date
May 27, 2026
π - Duration
More than 6 months
-
ποΈ - Location
Remote
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π - Contract
Unknown
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π - Security
Unknown
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π - Location detailed
Exton, PA
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π§ - Skills detailed
#Alation #Classification #Hugging Face #AI (Artificial Intelligence) #API (Application Programming Interface) #Deployment #FHIR (Fast Healthcare Interoperability Resources) #CMS (Content Management System) #Langchain #Scala #"ETL (Extract #Transform #Load)" #GitHub #C++ #ML (Machine Learning)
Role description
AI Product Builder | Healthcare AI Platform
Genzeon Corporation β Healthcare Division
Exton, PA / Remote | 0β3 years | Full-time
The short version: We use AI to process Medicare prior authorization documents β vision models, OCR, classification, RAG, clinical reasoning. 150K documents/year. Youβll own the product experience layer of that pipeline. Youβll design what happens when AI is confident,when itβs uncertain, and when itβs wrong.
What youβll do:
Own the product experience across a multi-model AI pipeline (classification βextraction β clinical QA β decision)
Design confidence thresholds, human review workflows, and escalation logic Work directly with ML engineers on model selection, prompt design, and architecture decisions
Build feedback loops between clinical reviewers and AI performance
Translate CMS/Medicare regulatory requirements into AI system behaviors
What you need:
At least one AI project youβve built that other people used (weβll ask what broke)
Understanding that LLMs are probabilistic and what that means for product design
Hands-on experience with one or more of: RAG, document AI, vision-language models,multi-agent systems
Ability to read code, navigate a repo, and have a technical conversation with engineers
Systems thinking β you care about what happens in the pipeline, not just the UI
Strong signals:
GitHub with AI projects β agents, RAG pipelines, fine-tuned models
Open-source contributions (LangChain, LlamaIndex, Hugging Face, llama.cpp)
AI hackathon participation
Familiarity with healthcare data (FHIR, X12, HL7)
Experience with on-prem GPU deployment, not just API calls
AI Product Builder | Healthcare AI Platform
Genzeon Corporation β Healthcare Division
Exton, PA / Remote | 0β3 years | Full-time
The short version: We use AI to process Medicare prior authorization documents β vision models, OCR, classification, RAG, clinical reasoning. 150K documents/year. Youβll own the product experience layer of that pipeline. Youβll design what happens when AI is confident,when itβs uncertain, and when itβs wrong.
What youβll do:
Own the product experience across a multi-model AI pipeline (classification βextraction β clinical QA β decision)
Design confidence thresholds, human review workflows, and escalation logic Work directly with ML engineers on model selection, prompt design, and architecture decisions
Build feedback loops between clinical reviewers and AI performance
Translate CMS/Medicare regulatory requirements into AI system behaviors
What you need:
At least one AI project youβve built that other people used (weβll ask what broke)
Understanding that LLMs are probabilistic and what that means for product design
Hands-on experience with one or more of: RAG, document AI, vision-language models,multi-agent systems
Ability to read code, navigate a repo, and have a technical conversation with engineers
Systems thinking β you care about what happens in the pipeline, not just the UI
Strong signals:
GitHub with AI projects β agents, RAG pipelines, fine-tuned models
Open-source contributions (LangChain, LlamaIndex, Hugging Face, llama.cpp)
AI hackathon participation
Familiarity with healthcare data (FHIR, X12, HL7)
Experience with on-prem GPU deployment, not just API calls






