AWS Data Engineer - (Amazon Bedrock + Amazon Q Business)

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an AWS Data Engineer (Amazon Bedrock + Amazon Q Business) on a 1-year remote contract, offering expertise in AWS AI services, Python, LLMs, and RAG implementation, with a focus on data quality and documentation workflows.
🌎 - Country
United States
πŸ’± - Currency
$ USD
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πŸ’° - Day rate
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πŸ—“οΈ - Date discovered
August 21, 2025
πŸ•’ - Project duration
More than 6 months
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🏝️ - Location type
Remote
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πŸ“„ - Contract type
Unknown
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πŸ”’ - Security clearance
Unknown
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πŸ“ - Location detailed
United States
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🧠 - Skills detailed
#Data Quality #Python #Data Engineering #Documentation #AWS (Amazon Web Services) #Programming #AI (Artificial Intelligence) #Metadata
Role description
Job Title: AWS Data Engineer (Amazon Bedrock + Amazon Q Business) Job Type: Remote Job Duration: 1 Years Contract Note: We are looking for candidate with strong experience with Amazon Q Business and Amazon Bedrock We are looking for strong expertise with Python and RAG Implementation Looking for candidate with LLM (Large Language Model) hands experience. Qualifications: β€’ Experience with AWS AI services, including Amazon Q Business and Amazon Bedrock β€’ Knowledge of effective prompt engineering and RAG implementation β€’ Understanding of content chunking, vectorization, and metadata strategies β€’ Proficiency in Python programming language β€’ Experience in data quality assessment and content standardization methodologies β€’ Experience working with large language models (LLMs) β€’ Advanced planning, organizational, problem-solving, analytical, decision-making and communication skills required β€’ Must be able to maintain a high degree of accuracy and confidentiality Responsibilities: β€’ Design and implement documentation ingestion workflows using Amazon Q Business and related AWS AI services β€’ Develop and maintain data preprocessing pipelines with focus on data quality improvement β€’ Lead initiatives to standardize and enhance documentation quality β€’ Assess response quality through ground truth analysis to optimize for business use cases β€’ Establish and monitor content quality metrics β€’ Collaborate with content owners to improve documentation structure and metadata