Element Technologies Inc
GenAI Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a GenAI Engineer in Chicago, IL, on a long-term contract. Key skills include expertise in Life Sciences data models, LLMs, knowledge graphs, and proficiency in Python, PyTorch/TensorFlow. On-site work is required.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
February 19, 2026
🕒 - Duration
Unknown
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🏝️ - Location
On-site
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Chicago, IL
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🧠 - Skills detailed
#HBase #RDF (Resource Description Framework) #Python #PyTorch #TigerGraph #Knowledge Graph #FHIR (Fast Healthcare Interoperability Resources) #Databases #Azure #Langchain #Cloud #TensorFlow #Neo4J #AI (Artificial Intelligence)
Role description
Gen AI Engineer
Chicago, IL
Long Term Contract
Responsibilities and Tech Stack
• Design and maintain enterprise knowledge graphs integrating clinical, medical, commercial, and patient services data.
• Model complex Life Sciences ontologies (e.g., SNOMED CT, ICD, RxNorm, MedDRA, HL7/FHIR).
• Build semantic layers enabling intelligent search, reasoning, and context-aware GenAI applications.
• Implement graph-based retrieval for RAG and contextual grounding of LLMs.Hands-on experience with:
• LLMs (OpenAI, Azure OpenAI, Llama, Claude, etc.)
• Knowledge Graphs (Neo4j, RDF/OWL, GraphDB, TigerGraph)
• RAG architectures, embeddings, vector databases
• Python, PyTorch/TensorFlow, LangChain, LLMOps
• Strong understanding of Life Sciences data models and patient services workflows.
• Experience with cloud platforms and MLOps tools.
Gen AI Engineer
Chicago, IL
Long Term Contract
Responsibilities and Tech Stack
• Design and maintain enterprise knowledge graphs integrating clinical, medical, commercial, and patient services data.
• Model complex Life Sciences ontologies (e.g., SNOMED CT, ICD, RxNorm, MedDRA, HL7/FHIR).
• Build semantic layers enabling intelligent search, reasoning, and context-aware GenAI applications.
• Implement graph-based retrieval for RAG and contextual grounding of LLMs.Hands-on experience with:
• LLMs (OpenAI, Azure OpenAI, Llama, Claude, etc.)
• Knowledge Graphs (Neo4j, RDF/OWL, GraphDB, TigerGraph)
• RAG architectures, embeddings, vector databases
• Python, PyTorch/TensorFlow, LangChain, LLMOps
• Strong understanding of Life Sciences data models and patient services workflows.
• Experience with cloud platforms and MLOps tools.
