

Synergy Technologies
Need Data Science Engineer with Python, AI/ML, LLMs, Vector DB Experiences :: CA (or) OH :: Onsite ::
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Science Engineer with expertise in Python, AI/ML, LLMs, and Vector DBs. It is onsite in Los Angeles, CA, or Mason, OH, with a focus on end-to-end ML lifecycle and production-grade ML engineering.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
June 18, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Mason, OH
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🧠 - Skills detailed
#Normalization #Data Science #Python #MongoDB #Deployment #AI (Artificial Intelligence) #ML (Machine Learning) #Scala #Databases #"ETL (Extract #Transform #Load)"
Role description
Role: Data Science Engineer
Location: Los Angeles, CA (or) Mason, OH
Must have skills:
• Python, Large Language Models (LLMs) (via LLM-based applications), Vector Databases, MongoDB
• Advanced Python development for ML/AI workloads
• End-to-end ML lifecycle: model training, evaluation, fine-tuning, and labeling/tagging workflows Generative Al systems design, including LLM-based application development
• Prompt engineering optimization for large language models
• Document Al pipelines: OCR/extraction, parsing, normalization, and text chunking for structured & unstructured data Embedding generation pipelines for semantic search and retrieval
• Vector similarity search implementation using vector databases
• ML model integration with Vector DBS and MongoDB
• Production-grade ML engineering: scalable, maintainable, and deployment-ready code
Role: Data Science Engineer
Location: Los Angeles, CA (or) Mason, OH
Must have skills:
• Python, Large Language Models (LLMs) (via LLM-based applications), Vector Databases, MongoDB
• Advanced Python development for ML/AI workloads
• End-to-end ML lifecycle: model training, evaluation, fine-tuning, and labeling/tagging workflows Generative Al systems design, including LLM-based application development
• Prompt engineering optimization for large language models
• Document Al pipelines: OCR/extraction, parsing, normalization, and text chunking for structured & unstructured data Embedding generation pipelines for semantic search and retrieval
• Vector similarity search implementation using vector databases
• ML model integration with Vector DBS and MongoDB
• Production-grade ML engineering: scalable, maintainable, and deployment-ready code






