Required 10 years of AI/ML Engineer to fine tune candidate embedding models on Client specific clinical documents for use in the enterprise patient document vector store. This role will evaluate model performance through a structured framework and ensure compliance with healthcare standards.
Responsibilities:
Select and fine tune one or two embedding models
Run models through the evaluation framework for accuracy and relevance.
Collaborate with physicians/clinicians to source and validate training data.
Ensure compliance with PHI consent checks and privacy regulations.
Contribute to enterprise scale vector store integration.
Requirements:
Strong background in AI/ML frameworks (PyTorch TensorFlow Hugging Face).
Experience with embedding models and vector databases.
Knowledge of RAG strategies and healthcare data compliance.
Ability to work with clinical teams to align models with domain needs.
Role : AI/ML Engineer Embedding Model Fine Tuning Location: Remote Duration :18 Months Job Summary: Required 10 years of AI/ML Engineer to fine tune candidate embedding models on Client specific clinical documents for use in the enterprise patient document vector store. This role will ev...
Role : AI/ML Engineer Embedding Model Fine Tuning
Location: Remote
Duration :18 Months
Job Summary:
Required 10 years of AI/ML Engineer to fine tune candidate embedding models on Client specific clinical documents for use in the enterprise patient document vector store. This role will evaluate model performance through a structured framework and ensure compliance with healthcare standards.
Responsibilities:
Select and fine tune one or two embedding models
Run models through the evaluation framework for accuracy and relevance.
Collaborate with physicians/clinicians to source and validate training data.
Ensure compliance with PHI consent checks and privacy regulations.
Contribute to enterprise scale vector store integration.
Requirements:
Strong background in AI/ML frameworks (PyTorch TensorFlow Hugging Face).
Experience with embedding models and vector databases.
Knowledge of RAG strategies and healthcare data compliance.
Ability to work with clinical teams to align models with domain needs.