Datum Technologies Group
Data Scientist
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Scientist (GenAI Full Stack Engineer) in Los Angeles, CA, with a C2C contract. Requires 8+ years in full-stack development, 2+ years in Generative AI, and expertise in data ingestion, indexing, and AI systems.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
600
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🗓️ - Date
April 17, 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
Los Angeles, CA
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🧠 - Skills detailed
#Data Ingestion #Data Science #AI (Artificial Intelligence) #Indexing
Role description
Role Details
Role Name: Data Scientist (GenAI Full Stack Engineer)
Location: Los Angeles, CA (USA)
Contract C2C
Job Description:
We are seeking an experienced GenAI Full Stack Engineer with strong expertise in building and scaling AI-driven applications.
• Key Responsibilities:8+ years of full-stack development experience, with at least 2+ years in Generative AI/LLM integration.
• Design, architect, and optimize agentic AI systems (e.g., tool-using agents, multi-step orchestration, and multi-agent frameworks).
• Integrate AI solutions with enterprise platforms and workflows.
• Lead end-to-end RAG (Retrieval-Augmented Generation) implementations, including:
• Data ingestion and preprocessing
• Chunking and embeddings
• Indexing and retrieval
• Orchestration and evaluation
• Drive GenAI model development, including training, fine-tuning, validation, and benchmarking.
Role Details
Role Name: Data Scientist (GenAI Full Stack Engineer)
Location: Los Angeles, CA (USA)
Contract C2C
Job Description:
We are seeking an experienced GenAI Full Stack Engineer with strong expertise in building and scaling AI-driven applications.
• Key Responsibilities:8+ years of full-stack development experience, with at least 2+ years in Generative AI/LLM integration.
• Design, architect, and optimize agentic AI systems (e.g., tool-using agents, multi-step orchestration, and multi-agent frameworks).
• Integrate AI solutions with enterprise platforms and workflows.
• Lead end-to-end RAG (Retrieval-Augmented Generation) implementations, including:
• Data ingestion and preprocessing
• Chunking and embeddings
• Indexing and retrieval
• Orchestration and evaluation
• Drive GenAI model development, including training, fine-tuning, validation, and benchmarking.
